Future wireless systems are envisioned to utilize the large spectra available at THz bands for wireless communications. Extremely massive multiple-input multiple-output (MIMO) antennas can be costly and power inefficient for wideband THz communications. An alternative antenna technology, which can achieve low-cost and power-efficient THz signaling, is based on leaky wave antennas (LWAs). In this paper, we explore the usage of the LWAs for wideband downlink multi-user THz communications. We propose a model for LWA-aided communication systems that faithfully captures the antenna operations. We show that LWAs yield frequency-dependent beams, where the equivalent wideband channel induces a dependence between angle, frequency, and spectral lobe width. We identify the LWA’s inherent frequency-selective beamsteering capabilities as motivating multi-band THz communications, in which subbands are allocated among users based on their relative angles. Then, we propose an alternating optimization algorithm for jointly optimizing the LWA configuration along with the spectral division and power allocation to maximize the achievable sum rate performance. Our numerical results show that a single LWA can generate diverse beampatterns, exhibiting performance comparable to costly MIMO architectures in wideband THz multi-user systems.

Federated learning (FL) is a leading approach for iterative learning using possibly private data available at edge devices. The federated operation gives rise to challenges in privacy leakage, which accumulates in learning, and communication latency. These limitations are often individually mitigated by the introduction of privacy preserving noise and user-selection policies, typically at the cost of accuracy. In this work, we propose Privacy-aware Active User SElection (PAUSE), which balances the trade-off between privacy accumulation, communication latency, and optimization of the learned model, via dedicated user selection. This triplet is used to construct a reward (cost function), according to which a multi-armed bandit (MAB)-based algorithm dynamically chooses a subset of users in each round, while guaranteeing bounded accumulated privacy leakage. We establish a theoretical analysis, systematically showing that the reward growth rate of PAUSE follows the best-known rate in MAB literature. While the privacy guarantees hold by the construction of PAUSE, we numerically validate its associated improved latency and accuracy gains in different experimental settings of FL.

Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. It typically involves a set of heterogeneous devices locally training neural network (NN) models in parallel with periodic centralized aggregations. As some of the devices may have limited computational resources and varying availability, FL latency is highly sensitive to stragglers. Conventional approaches discard incomplete intra-model updates done by stragglers, alter the amount of local workload and architecture, or resort to asynchronous settings; which all affect the trained model performance under tight training latency constraints. In this work, we propose stragglers-aware layerwise federated learning (SALF) that leverages the optimization procedure of NNs via backpropagation to update the global model in a layer-wise fashion. SALF allows stragglers to synchronously convey partial gradients, having each layer of the global model be updated independently with a different contributing set of users. We provide a theoretical analysis, establishing convergence guarantees for the global model under mild assumptions on the distribution of the participating devices, revealing that SALF converges at the same asymptotic rate as FL with no timing limitations. This insight is matched with empirical observations, demonstrating the performance gains of SALF compared to alternative mechanisms mitigating the device heterogeneity gap in FL.

Federated learning (FL) is an emerging paradigm that allows a central server to train machine learning models using remote users' data. Despite its growing popularity, FL faces challenges in preserving the privacy of local datasets, its sensitivity to poisoning attacks by malicious users, and its communication overhead, especially in large-scale networks. These limitations are often individually mitigated by local differential privacy (LDP) mechanisms, robust aggregation, compression, and user selection techniques, which typically come at the cost of accuracy. In this work, we presentcompressed private aggregation (CPA), allowing massive deployments to simultaneously communicate at extremely low bit rates while achieving privacy, anonymity, and resilience to malicious users. CPA randomizes a codebook for compressing the data into a few bits using nested lattice quantizers, while ensuring anonymity and robustness, with a subsequent perturbation to hold LDP. CPA-aided FL is proven to converge in the same asymptotic rate as FL without privacy, compression, and robustness considerations, while satisfying both anonymity and LDP requirements. These analytical properties are empirically confirmed in a numerical study, where we demonstrate the performance gains of CPA compared with separate mechanisms for compression and privacy, as well as its robustness in mitigating the harmful effects of malicious users.

Lattice quantization implements vector quantization with a simple structured formulation, that is fully determined by the lattice generator matrix and a distance metric. The conventional approach constructs lattices for quantization by minimizing a bound on the rate-distortion tradeoff, which holds for non-overloaded quantizers, while in practice, overloading prevention typically affects performance. In this work we propose a novel technique for constructing lattice that considers possibly overloaded quantizers, for which we learn the lattice generator matrix by directly evaluating the distortion at its output. For training purposes, we convert the continuous-to-discrete quantizer mapping into a differentiable machine learning model, optimized in an unsupervised manner to best fit the data. Subsequently, the data-driven lattice is fixed and ordinarily combined into the quantization process. We provide numerical studies showing that our method attains improved performance compared with alternative lattice designs for various dimensions, and generalizes well to unseen data.

Federated learning (FL) allows a central server to train a model using remote users’ data. FL faces challenges in preserving the local datasets privacy and in its communication overhead; which is considerably dominant in large-scale networks. These limitations are often mitigated individually by local differential privacy (LDP) mechanisms, compression, and user-selection techniques, which often come at the cost of accuracy. In this work we present compressed private aggregation (CPA), which allows massive deployments to simultaneously communicate at extremely low bit-rates while achieving privacy, anonymity, and resilience to malicious users. CPA randomizes a code-book for compressing the data into a few bits, ensuring anonymity and robustness, with a subsequent perturbation to hold LDP. We provide both a theoretical analysis and a numerical study, demonstrating the performance gains of CPA compared with separate mechanisms for compression and privacy.

Federated learning (FL) is an emerging paradigm for training machine learning models using possibly private data available at edge devices. Among the key challenges associated with FL are first the need to preserve the privacy of the local data sets, and second the communication load due to the repeated exchange of updated models; both are often tackled individually with methods whose operation distorts the updated models, e.g., local differential privacy (LDP) mechanisms and lossy compres- sion, respectively. In this work we propose a method for joint privacy enhancement and quantization (JoPEQ), unifying lossy compression and privacy enhancement for FL. JoPEQ utilizes universal vector quantization, where distortion is statistically equivalent to additive noise, and augments the compression distortion with dedicated privacy preserving noise to simultaneously achieve compression and a desired privacy level. We numerically demonstrate that JoPEQ reduces the overall distortion compared to individual LDP and compression, which is translated into improved trained models.