As machine learning becomes more pervasive and data privacy regulations evolve, the ability to remove private or copyrighted information from trained models is becoming an increasingly critical requirement. Existing unlearning methods often rely on the assumption of having access to the entire training dataset during the forgetting process. However, this assumption may not hold true in practical scenarios where the original training data may not be accessible, i.e., the source-free setting. To address this challenge, we focus on the source-free unlearning scenario, where an unlearning algorithm must be capable of removing specific data from a trained model without requiring access to the original training dataset. Building on recent work, we present a method that can estimate the Hessian of the unknown remaining training data, a crucial component required for efficient unlearning. Leveraging this estimation technique, our method enables efficient zero-shot unlearning while providing robust theoretical guarantees on the unlearning performance, while maintaining performance on the remaining data. Extensive experiments over a wide range of datasets verify the efficacy of our method.

Incorporating transformer models into edge devices poses a significant challenge due to the computational demands of adapting these large models across diverse applications. Parameter-efficient tuning (PET) methods (e.g. LoRA, Adapter, Visual Prompt Tuning, etc.) allow for targeted adaptation by modifying only small parts of the transformer model. However, adapting to dynamic unlabeled target distributions at the test time remains complex. To address this, we introduce AdMiT: Adaptive Multi-Source Tuning in Dynamic Environments. AdMiT innovates by pre-training a set of PET modules, each optimized for different source distributions or tasks, and dynamically selecting and integrating a sparse subset of relevant modules when encountering a new, few-shot, unlabeled target distribution. This integration leverages Kernel Mean Embedding (KME)-based matching to align the target distribution with relevant source knowledge efficiently, without requiring additional routing networks or hyperparameter tuning. AdMiT achieves adaptation with a single inference step, making it particularly suitable for resource-constrained edge deployments. Furthermore, AdMiT preserves privacy by performing an adaptation locally on each edge device, without the need for data exchange. Our theoretical analysis establishes guarantees for AdMiT’s generalization, while extensive benchmarks demonstrate that AdMiT consistently outperforms other PET methods across a range of tasks, achieving robust and efficient adaptation.

Federated learning (FL) allows multiple data-owners to collaboratively train machine learning models by exchanging local gradients, while keeping their private data on-device. To simultaneously enhance privacy and training efficiency, recently parameter-efficient fine-tuning (PEFT) of large-scale pretrained models has gained substantial attention in FL. While keeping a pretrained (backbone) model frozen, each user fine-tunes only a few lightweight modules to be used in conjunction, to fit specific downstream applications. Accordingly, only the gradients with respect to these lightweight modules are shared with the server. In this work, we investigate how the privacy of the fine-tuning data of the users can be compromised via a malicious design of the pretrained model and trainable adapter modules. We demonstrate gradient inversion attacks on a popular PEFT mechanism, the adapter, which allow an attacker to reconstruct local data samples of a target user, using only the accessible adapter gradients. Via extensive experiments, we demonstrate that a large batch of fine-tuning images can be retrieved with high fidelity. Our attack highlights the need for privacy-preserving mechanisms for PEFT, while opening up several future directions. Our code is available at https://github.com/info-ucr/PEFTLeak.

Computation offloading enables resource-limited users to delegate resource-intensive processing tasks to more powerful edge devices (workers). In doing so, a major concern is to protect the privacy of sensitive data against untrusted devices. In this work, we propose a secure computation offloading framework to address this challenge. To enable adaptability to resource heterogeneities across the workers, we then propose a hierarchical combinatorial neural multi-armed bandit mechanism for secure computation resource allocation. Our framework provides information-theoretic privacy guarantees for both the sensitive user data and computation results, while minimizing system latency of secure computations in the presence of heterogeneous resource availability. Our experiment results show that the proposed framework achieves$3 \times$faster task completion time compared to conventional secure offloading baselines and is$13 \times$faster than local processing at the user.

Federated Learning (FL) enables model training across decentralized clients while preserving data privacy. However, bandwidth constraints limit the volume of information exchanged, making communication efficiency a critical challenge. In addition, non-IID data distributions require fairness-aware mechanisms to prevent performance degradation for certain clients. Existing sparsification techniques often apply fixed compression ratios uniformly, ignoring variations in client importance and bandwidth. We propose Fed-Band, a dynamic bandwidth allocation framework that prioritizes clients based on their contribution to the global model. Unlike conventional approaches, FedBand does not enforce uniform client participation in every communication round. Instead, it allocates more bandwidth to clients whose local updates deviate significantly from the global model, enabling them to transmit a greater number of parameters. Clients with less impactful updates contribute proportionally less or may defer transmission, reducing unnecessary overhead while maintaining generalizability. By optimizing the trade-off between communication efficiency and learning performance, FedBand substantially reduces transmission costs while preserving model accuracy. Experiments on non-IID CIFAR-10 and UTMobileNet2021 datasets, demonstrate that FedBand achieves up to 99.81% bandwidth savings per round while maintaining accuracies close to that of an unsparsified model (80% on CIFAR-10, 95% on UTMobileNet), despite transmitting less than 1% of the model parameters in each round. Moreover, FedBand accelerates convergence by 37.4%, further improving learning efficiency under bandwidth constraints. Mininet emulations further show a 42.6% reduction in communication costs and a 65.57% acceleration in convergence compared to baseline methods, validating its real-world efficiency. These results demonstrate that adaptive bandwidth allocation can significantly enhance the scalability and communication efficiency of federated learning, making it more viable for real-world, bandwidth-constrained networking environments.

With the growing adoption of data privacy regulations, the ability to erase private or copyrighted information from trained models has become a crucial requirement. Traditional unlearning methods often assume access to the complete training dataset, which is unrealistic in scenarios where the source data is no longer available. To address this challenge, we propose a certified unlearning framework that enables effective data removal without access to the original training data samples. Our approach utilizes a surrogate dataset that approximates the statistical properties of the source data, allowing for controlled noise scaling based on the statistical distance between the two. While our theoretical guarantees assume knowledge of the exact statistical distance, practical implementations typically approximate this distance, resulting in potentially weaker but still meaningful privacy guarantees. This ensures strong guarantees on the model’s behavior post-unlearning while maintaining its overall utility. We establish theoretical bounds, introduce practical noise calibration techniques, and validate our method through extensive experiments on both synthetic and real-world datasets. The results demonstrate the effectiveness and reliability of our approach in privacy-sensitive settings.

Secure aggregation is an information-theoretic mechanism for gradient aggregation in federated learning, to aggregate the local user gradients without revealing them in the clear. In this work, we study secure aggregation under gradient sparsification constraints, for resource-limited wireless networks, where only a small fraction of local parameters are aggregated from each user during training (as opposed to the full gradient). We demonstrate that conventional mechanisms can reveal sensitive user data when aggregating sparsified gradients, due to the auxiliary coordinate information shared during sparsification, even when the individual gradients are not disclosed in the clear. We then propose a coordinate-hiding sparsified secure aggregation mechanism to address this challenge, which hides both the gradient parameters and the associated coordinates under formal information-theoretic privacy guarantees. Our framework reduces the communication overhead of conventional secure aggregation baselines by an order of magnitude without compromising model accuracy.

Secure aggregation (SA) is a privacy-enhancing framework for federated learning, to aggregate the local gradient updates from the users without revealing them in the clear. Conventional SA frameworks are built under the assumption of homogeneous computational resources across the users, where users are bound to train a local model whose dimensions are as large as the global model, preventing resource-limited users from participating in training. In this work, we propose a novel secure submodel training framework to address this challenge, where users train and communicate partial submodels through an adaptable secure aggregation mechanism during training. Our framework enables the participation of all users with varying computation and communication resources, while ensuring formal information-theoretic privacy guarantees for the individual local updates.

Privacy-preserving coded computing is a popular framework for multiple data-owners to jointly train machine learning models, with strong end-to-end information-theoretic privacy guarantees for the local data. A major challenge against the scalability of current approaches is their communication overhead, which is quadratic in the number of users. Towards addressing this challenge, we present SCALR, a communication-efficient collaborative learning framework for training logistic regression models. To do so, we introduce a novel coded computing mechanism, by decoupling the communication-intensive encoding operations from real-time training, and offloading the former to a data-independent offline phase, where the communicated variables are independent from training data. As such, the offline phase can be executed proactively during periods of low network activity. Communication complexity of the data-dependent (online) training operations is only linear in the number of users, greatly reducing the quadratic state-of-the-art. Our theoretical analysis presents the information-theoretic privacy guarantees, and shows that SCALR achieves the same performance guarantees as the state-of-the-art, in terms of adversary resilience, robustness to user dropouts, and model convergence. Through extensive experiments, we demonstrate up to$80\times $reduction in online communication overhead, and$6\times $speed-up in the wall-clock training time compared to the state-of-the-art.

Clustered federated learning is a popular paradigm to tackle data heterogeneity in federated learning, by training personalized models for groups of users with similar data distributions. A critical challenge is to protect the privacy of individual user updates, as the latter can reveal extensive information about sensitive local datasets. To do so, a recent promising approach is information-theoretic secure aggregation, where parties learn the aggregate (sum) of user updates, but no further information is revealed about the individual updates. In this work, we present the first single-server secure aggregation framework in the context of clustered federated learning, to learn the aggregate of user updates for any clustering of users, but without learning any information about the local updates or cluster identities. Our framework can achieve linear communication complexity under formal information-theoretic privacy guarantees, while providing key trade-offs between communication and computation complexity, adversary tolerance, and resilience to user dropouts.

Secure aggregation is an information-theoretic mechanism for gradient aggregation in federated learning, to aggregate the local user gradients without revealing them in the clear. In this work, we study secure aggregation under gradient sparsification constraints, for resource-limited wireless networks, where only a small fraction of local parameters are aggregated from each user during training (as opposed to the full gradient). We first identify the vulnerabilities of conventional secure aggregation mechanisms under gradient sparsification. We show that conventional mechanisms can reveal sensitive user data when aggregating sparsified gradients, due to the auxiliary coordinate information shared during sparsification, even when the individual gradients are not disclosed in the clear. We then propose TinySecAgg, a novel coordinate-hiding sparsified secure aggregation mechanism to address this challenge, under formal information-theoretic privacy guarantees. Our framework reduces the communication overhead of conventional secure aggregation baselines by an order of magnitude (up to 22.5×) without compromising model accuracy.

Privacy-preserving machine learning has achieved breakthrough advances in collaborative training of machine learning models, under strong information-theoretic privacy guarantees. Despite the recent advances, communication bottleneck still remains as a major challenge against scalability in neural networks. To address this challenge, this paper presents the first scalable multi-party neural network training framework with linear communication complexity, significantly improving over the quadratic state-of-the-art, under strong end-to-end information-theoretic privacy guarantees. Our contribution is an iterative coded computing mechanism with linear communication complexity, termed Double Lagrange Coding, which allows iterative scalable multi-party polynomial computations without degrading the parallelization gain, adversary tolerance, and dropout resilience throughout the iterations. While providing strong multi-round information-theoretic privacy guarantees, our framework achieves equal adversary tolerance, resilience to user dropouts, and model accuracy to the state-of-the-art, while reducing the communication overhead from quadratic to linear. In doing so, our framework addresses a key technical challenge in collaborative privacy-preserving machine learning, while paving the way for large-scale privacy-preserving iterative algorithms for deep learning and beyond.

Collaborative machine learning enables privacy-preserving training of machine learning models without collecting sensitive client data. Despite recent breakthroughs, communication bottleneck is still a major challenge against its scalability to larger networks. To address this challenge, in this work we propose PICO, the first collaborative learning framework with linear communication complexity, significantly improving over the quadratic state-of-the-art, under formal information-theoretic privacy guarantees. Theoretical analysis demonstrates that PICO slashes the communication cost while achieving equal computational complexity, adversary resilience, robustness to client dropouts, and model accuracy to the state-of-the-art. Extensive experiments demonstrate up to 91× reduction in the communication overhead, and up to 8× speed-up in the wall-clock training time compared to the state-of-the-art. As such, PICO addresses a key technical challenge in multi-party collaborative learning, paving the way for future large-scale privacy-preserving learning frameworks.

Over-the-air federated learning (FL) is a recent paradigm to address the communication bottleneck of FL, where a machine learning model is trained by aggregating the local gradients directly in the wireless medium. On the other hand, due to the inherent data heterogeneity across wireless users, training a single model to serve all users can severely degrade individual user performance. Towards addressing this challenge, in this work we proposeover-the-air clustered FL, where multiple models are trained concurrently over-the-air, and each model is adapted gradually to a group of users with similar data distributions. We introduce AirCluster, an over-the-air clustered FL framework with coordinated zero-forcing MIMO beamforming, along with a sketching-based dimensionality reduction mechanism to enable over-the-air training with limited number of antennas. Our theoretical analysis provides formal convergence guarantees for the trained models, while identifying the key performance trade-offs in terms of the convergence rate, compression ratio, channel quality, and the number of antennas. Through extensive experiments on multiple datasets, we observe significant increase in the test accuracy for individual users over state-of-the-art FL benchmarks. Our results demonstrate over-the-air FL to be a promising approach in addressing the communication bottleneck of FL, even under severe data heterogeneity.

Secure aggregation is a critical component in federated learning (FL), which enables the server to learn the aggregate model of the users without observing their local models. Conventionally, secure aggregation algorithms focus only on ensuring the privacy of individual users in a single training round. We contend that such designs can lead to significant privacy leakages over multiple training rounds, due to partial user selection/participation at each round of FL. In fact, we show that the conventional random user selection strategies in FL lead to leaking users' individual models within number of rounds that is linear in the number of users. To address this challenge, we introduce a secure aggregation framework, Multi-RoundSecAgg, with multi-round privacy guarantees. In particular, we introduce a new metric to quantify the privacy guarantees of FL over multiple training rounds, and develop a structured user selection strategy that guarantees the long-term privacy of each user (over any number of training rounds). Our framework also carefully accounts for the fairness and the average number of participating users at each round. Our experiments on MNIST, CIFAR-10 and CIFAR-100 datasets in the IID and the non-IID settings demonstrate the performance improvement over the baselines, both in terms of privacy protection and test accuracy.

Collaborative machine learning enables privacy-preserving training of machine learning models without collecting sensitive client data. Despite recent breakthroughs, communication bottleneck is still a major challenge against its scalability to larger networks. To address this challenge, we propose PICO, the first collaborative learning framework with linear communication complexity, significantly improving over the quadratic state-of-the-art, under formal information-theoretic privacy guarantees. Theoretical analysis demonstrates that PICO slashes the communication cost while achieving equal computational complexity, adversary resilience, robustness to client dropouts, and model accuracy to the state-of-the-art. Extensive experiments demonstrate up to 91x reduction in the communication overhead, and up to 7x speed-up in the wall-clock training time compared to the state-of-the-art. As such, PICO addresses a key technical challenge in multi-party collaborative learning, paving the way for future large-scale privacy-preserving learning frameworks.

Privacy-preserving machine learning has achieved exciting breakthroughs for collaboratively training machine learning models under strong information-theoretic privacy guarantees. Despite the recent advances, communication bottleneck still remains as a major challenge against scalability to large neural networks. To address this challenge, in this work we introduce CLOVER, the first multi-party neural network training framework with linear communication complexity, significantly improving over the quadratic state-of-the-art, under strong end-to-end information-theoretic privacy guarantees. CLOVER builds on a novel degree reduction mechanism with linear communication complexity, termed Double Lagrange Coding, for coded computing. While providing strong multi-round information-theoretic privacy guarantees, CLOVER achieves equal adversary tolerance, resilience to user dropouts, and model accuracy as the state-of-the-art, while significantly cutting down the communication overhead. In doing so, CLOVER addresses a key technical challenge in collaborative neural network training, paving the way for large-scale privacy-aware deep learning applications.

Clustered federated learning is a popular paradigm to tackle data heterogeneity in federated learning, by training personalized models for groups of users with similar data distributions. A critical challenge is to protect the privacy of individual user updates, as the latter can reveal extensive information about sensitive local datasets. To do so, a recent promising approach is information-theoretic secure aggregation, where parties learn the aggregate (sum) of user updates, but no further information is revealed about the individual updates. In this work, we present the first secure aggregation frameworks in the context of clustered federated learning, to learn the aggregate of user updates for any clustering of users, but without learning any information about the local updates or cluster identities. Our frameworks can achieve linear communication complexity under formal information-theoretic privacy guarantees, while providing key trade-offs between communication and computation complexity, adversary tolerance, and resilience to user dropouts.

Secure aggregation is a privacy-aware protocol for model aggregation in federated learning. A major challenge of conventional secure aggregation protocols is their large communication overhead. Towards addressing this challenge, in this work we propose the first gradient sparsification framework for communication-efficient secure aggregation, which allows aggregation of sparsified local gradients from a large number of users, without revealing the individual local gradient parameters in the clear. We provide the theoretical performance guarantees of the proposed framework in terms of the communication efficiency, resilience to user dropouts, and model convergence. We further evaluate the performance of our framework through large-scale experiments in a distributed network with up to 100 users, and demonstrate a significant reduction in the communication over-head compared to conventional secure aggregation benchmarks.

Federated learning is a distributed framework for training a machine learning model over the data stored by wireless devices. A major challenge in doing so is the communication overhead from the devices to the server. Over-the-air federated learning is a recent framework to address this challenge, which utilizes the superposition property of the wireless multiple access channel to enable computations to be performed in the wireless medium. Current over-the-air aggregation frameworks, on the other hand, train a single model for all users, which can degrade performance in heterogeneous environments where the data distributions of the users can differ from one another. This work presents a personalized over-the-air federated learning framework towards addressing this challenge. Our experiments demonstrate significant performance improvement in terms of the test accuracy over conventional federated learning.

This paper provides a first study of utilizing energy harvesting for sustainable machine learning in distributed networks. We consider a distributed learning setup in which a machine learning model is trained over a large number of devices that can harvest energy from the ambient environment, and develop a practical learning framework with theoretical convergence guarantees. We demonstrate through numerical experiments that the proposed framework can significantly out- perform energy-agnostic benchmarks. Our framework is scalable, requires only local estimation of the energy statistics, and can be applied to a wide range of distributed training settings, including machine learning in wireless networks, edge computing, and mobile internet of things.

Potential environmental impact of machine learning in large-scale wireless networks is a major challenge for the sustainability of next-generation intelligent systems. Federated learning is a recent framework for communication-efficient training of machine learning models over the data collected, stored, and processed by millions of wireless devices. In this paper, we introduce a sustainable machine learning framework for federated learning, using rechargeable devices that can collect energy from the ambient environment. In particular, we propose a practical federated learning framework that utilizes intermittent energy arrivals for training, with provable convergence guarantees. Our framework can be applied to both cross-device and cross-silo federated learning settings, including federated learning in wireless edge networks and the Internet-of-Things. Our experiments demonstrate that the proposed framework can provide significant performance improvement over the benchmark energy-agnostic federated learning settings.

Secure federated learning is a privacy-preserving framework to improve machine learning models by training over large volumes of data collected by mobile users. This is achieved through an iterative process where, at each iteration, users update a global model using their local datasets. Each user then masks its local update via random keys, and the masked models are aggregated at a central server to compute the global model for the next iteration. As the local updates are protected by random masks, the server cannot observe their true values. This presents a major challenge for the resilience of the model against adversarial (Byzantine) users, who can manipulate the global model by modifying their local updates or datasets. Towards addressing this challenge, this paper presents the first single-server Byzantine-resilient secure aggregation framework (BREA) for secure federated learning. BREA is based on an integrated stochastic quantization, verifiable outlier detection, and secure model aggregation approach to guarantee Byzantine-resilience, privacy, and convergence simultaneously. We provide theoretical convergence and privacy guarantees and characterize the fundamental trade-offs in terms of the network size, user dropouts, and privacy protection. Our experiments demonstrate convergence in the presence of Byzantine users, and comparable accuracy to conventional federated learning benchmarks.

We consider a collaborative learning scenario in which multiple data-owners wish to jointly train a logistic regression model, while keeping their individual datasets private from the other parties. We propose COPML, a fully-decentralized training framework that achieves scalability and privacy-protection simultaneously. The key idea of COPML is to securely encode the individual datasets to distribute the computation load effectively across many parties and to perform the training computations as well as the model updates in a distributed manner on the securely encoded data. We provide the privacy analysis of COPML and prove its convergence. Furthermore, we experimentally demonstrate that COPML can achieve significant speedup in training over the benchmark protocols. Our protocol provides strong statistical privacy guarantees against colluding parties (adversaries) with unbounded computational power, while achieving up to $16\times$ speedup in the training time against the benchmark protocols.

This paper considers the graph signal sampling problem when some of the selected samples are lost or unavailable due to sensor failures or adversarial erasures. We formulate a robust graph signal sampling problem where only a subset of selected samples are received, and the goal is to maximize the worst-case performance. We propose a novel greedy robust sample selection algorithm and study its performance guarantees. Our numerical results demonstrate the performance improvement of the proposed algorithm over the existing schemes.

Lossy coding of correlated sources over a multiple access channel (MAC) is studied. First, a joint source-channel coding scheme is presented when the decoder has correlated side information. Next, the optimality of separate source and channel coding that emerges from the availability of a common observation at the encoders or side information at the encoders and the decoder is investigated. It is shown that separation is optimal when the encoders have access to a common observation whose lossless recovery is required at the decoder, and the two sources are independent conditioned on this common observation. Optimality of separation is also proved when the encoder and the decoder have access to shared side information conditioned on which the two sources are independent. These separation results obtained in the presence of side information are then utilized to provide a set of necessary conditions for the transmission of correlated sources over a MAC without side information. Finally, by specializing the obtained necessary conditions to the transmission of binary and Gaussian sources over a MAC, it is shown that they can potentially be tighter than the existing results in the literature, providing a novel converse for this fundamental problem.

We study the lossy communication of correlated sources over a multiple access channel (MAC). In particular, we provide a new set of necessary conditions for the achievability of a distortion pair over a given channel. The necessary conditions are then specialized to the case of bivariate Gaussian sources and doubly symmetric binary sources over a Gaussian multiple access channel. Our results indicate that the new necessary conditions provide the tightest conditions to date in certain cases.

We study the interactive compression of an arbitrary function of two discrete sources with zero-error. The information on the joint distribution of the sources available at the two sides is asymmetric, in that one user knows the true distribution, whereas the other user observes a different distribution. This paper considers the minimum worst-case zero-error codeword length under such asymmetric prior distributions. We investigate the cases for which reconciling the information mismatch is better or worse than not reconciling it, but instead using an encoding scheme that ensures zero-error with possibly increased communication rate. Our results indicate a reconciliation-communication tradeoff and that there exist cases for which partially reconciling the mismatched information is better than both perfect reconciliation and no reconciliation.

We study how to communicate semantic information in the presence of an agent that can influence the decoder by providing side information. The agent's true intentions, which may be adversarial or helpful, is unknown to the communicating parties. Actions taken by the agent are governed by its intentions, and they may improve or deteriorate the communication performance. We characterize the optimal transmission policies to minimize the end-to-end average semantic error, i.e., difference between the meanings of intended and recovered messages, under the uncertainty in the agent's true intentions. We formulate the semantic communication problem as a Bayesian game, and investigate the conditions under which a pure strategy Bayesian Nash equilibrium exists. We then explore the structure of the encoding and decoding functions under the mixed strategy Bayesian Nash equilibrium, which for the semantic communication problem at hand always exists. Our results show that the optimal policies are strongly influenced by the belief the parties hold about the agent's true intention.

We study lossy communication of correlated sources over a multiple access channel. In particular, we provide a joint source-channel coding scheme for transmitting correlated sources with decoder side information, and study the conditions under which separate source and channel coding is optimal. For the latter, the encoders and/or the decoder have access to a common observation conditioned on which the two sources are independent. By establishing necessary and sufficient conditions, we show the optimality of separation when the encoders and the decoder both have access to the common observation. We also demonstrate that separation is optimal when only the encoders have access to the common observation whose lossless recovery is required at the decoder. As a special case, we study separation for sources with a common part. Our results indicate that side information can have significant impact on the optimality of source-channel separation in lossy transmission.

This paper studies the impact of side information on the lossy compression of a remote source, one which is indirectly accessed by the encoder. In particular, we identify the conditions under which sharing side information between the encoder and the decoder may be superior or inferior to having two-sided, i.e., correlated but not identical, side information. As a special case, we characterize the optimal rate-distortion function for a direct binary source with two-sided information by proposing an achievable scheme and proving a converse. This example suggests a hierarchy on the impact of side information, in that the performance is mainly determined by how well the decoder learns about the source and then by how well the encoder learns about the decoder's observation.

Online social communities often exhibit complex relationship structures, ranging from close friends to political rivals. As a result, persons are influenced by their friends and foes differently. Network applications can benefit from accompanying these structural differences in propagation schemes. In this paper, we study the optimal influence propagation policies for networks with positive and negative relationship types. We tackle the problem of minimizing the end-to-end propagation cost of influencing a target person in favor of an idea by utilizing the relationship types in the underlying social graph. The propagation cost is incurred by social and physical network dynamics such as frequency of interaction, the strength of friendship and foe ties, propagation delay or the impact factor of the propagating idea. We extend this problem by incorporating the impact of message deterioration and ignorance. We demonstrate our results in both a controlled environment and the Epinions dataset. Our results show that judicious propagation schemes lead to a significant reduction in the average cost and complexity of influence propagation compared to naïve myopic algorithms.

Semantics of communicated data can lead to conclusions with varying degrees of priorities. Depending on the interests of the communicating parties, some facts lead to conclusions that carry a high risk when ignored, and others may not be worth the resources to share the facts leading to those uninteresting conclusions. This paper studies the worst-case semantic data compression problem for sharing facts that lead to conclusions with such varying priorities. We establish the performance bounds by utilizing the partial dependencies between the ideas and the priority distributions on the conclusions. We show that multiple term descriptions of the facts and conclusions improve the compression performance when combined with judicious partitioning of the fact space.

This paper presents a novel cross-tier interference management solution for coexisting two-tier networks by exploiting cognition and coordination between tiers via the use of agile radios. The cognitive users sense their environment to determine the receivers they are interfering with, and adapt to it by designing their precoders using interference alignment (IA) in order to avoid causing performance degradation to nearby receivers. The proposed approach judiciously chooses the set of users to be aligned at each receiver as a subset of the cross-tier interferers, hence is termed selective IA. The proposed solution includes identification of the subspace in which cross-tier interference signals would be aligned followed by a distributed algorithm to identify the precoders needed at the selected interferers. The intra-tier interference is then dealt with using minimum mean squared error (MMSE) interference suppression. Numerical results demonstrate the effectiveness of selective IA for both uplink and downlink interference management.

This paper considers uplink interference management for two-tier cellular systems by way of Interference Alignment (IA). In order to manage the uplink interference caused by macrocell users at the femtocell base stations (FBS), cooperation between macrocell users with the closest femtocell base stations is proposed with the goal of aligning the received signals of macrocell users in the same subspace at multiple FBSs. The precoder design for macrocell users is accomplished using successive semidefinite programming relaxations. The proposed solution aims to minimize the cross-tier interference leaked to the femtocells while providing the macrocell users with a minimum received signal to interference plus noise ratio (SINR) at the macrocell base station (MBS). Intra-tier femtocell interference is dealt with minimum mean squared error (MMSE) interference suppression. Numerical results demonstrate that the proposed two-tier interference management approach improves the performance of femtocell users, while maintaining the desired quality of the communication channel of macrocell users.

An interference limited multitier multiuser MIMO cellular uplink is considered. Specifically, an interference management scheme is proposed where interference from subsets of macrocell users is aligned at the femtocell base stations in order to ensure acceptable service for the femtocell users. The scheme employs interference alignment (IA) at each femtocell base station (FBS), to the set of macrocell users (MU) that are causing the high interference specifically at that FBS, and hence is termed selective IA. The proposed IA algorithm determines the interference subspaces at each FBS and precoders for each MU in a distributed fashion. Numerical results demonstrate the performance advantage of selective IA.

This paper proposes a method for applying the idea of Interference Alignment (IA) in femtocell networks. In order to manage the uplink interference caused by macrocell users at the femtocell base stations (FBS), cooperation between macrocell users with the closest femtocell base stations could be used to align the received signals of macrocell users in the same subspace at multiple FBS simultaneously. We develop a method to apply IA while providing the QoS requirements of macrocell users, in terms of minimum received SINR at the macrocell base station (MBS). With this approach, the BER performance of femtocell users is shown to improve, while maintaining the quality of the communication channel of macrocell users.