As federated learning (FL) becomes more widespread, there is growing interest in its decentralized variants. Decentralized FL leverages the benefits of fast and energy-efficient device-to-device communications to obviate the need for a central server. However, this opens the door to new security vulnerabilities as well. While FL security has been a popular research topic, the role of adversarial node placement in decentralized FL remains largely unexplored. This article addresses this gap by evaluating the impact of various coordinated adversarial node placement strategies on decentralized FL’s model training performance. We adapt two threads of placement strategies to this context: 1) maximum span-based algorithms and 2) network centrality-based approaches. Building on them, we propose a novel attack strategy, MaxSpAN-FL, which is a hybrid between these paradigms that adjusts node placement probabilistically based on network topology characteristics. Numerical experiments demonstrate that our attack consistently induces the largest degradation in decentralized FL models compared with baseline schemes across various network configurations and numbers of coordinating adversaries. We also provide theoretical support for why eigenvector centrality-based attacks are suboptimal in decentralized FL. Overall, our findings provide valuable insights into the vulnerabilities of decentralized FL systems, setting the stage for future research aimed at developing more secure and robust decentralized FL frameworks.

The adversarial wiretap channel of type II (AWTC-II) is a communication channel that can a) read a fraction of the transmitted symbols up to a given bound and b) induce both errors and erasures in a fraction of the symbols up to given bounds. The channel is controlled by an adversary who can freely choose the locations of the symbol reads, errors and erasures via a process with unbounded computational power. The AWTC-II is an extension of Ozarow’s and Wyner’s wiretap channel of type II to the adversarial channel setting. The semantic-secrecy (SS) capacity of the AWTC-II is partially known, where the best-known lower bound is non-constructive and proven via a random coding argument that uses a large number (that is, exponential in blocklengthn) of random bits to describe the random code. In this work, we establish a new derandomization result in which we match the best-known lower bound via a non-constructive random code that uses onlyO(n2) random bits. Unlike fully random codes, our derandomized code admits an efficient encoding algorithm and benefits from some linear structure. Our derandomization result is a novel application ofrandom pseudolinear codes– a class of non-linear codes first proposed for applications outside the AWTC-II setting, which havek-wise independent codewords wherekis a design parameter. As the key technical tool in our analysis, we provide a novel concentration inequality for sums of random variables with limited independence, as well as a soft-covering lemma similar to that of Goldfeld, Cuff and Permuter that holds for random codes withk-wise independent codewords.

As Federated Learning (FL) grows in popularity, new decentralized frameworks are becoming widespread. These frameworks leverage the benefits of decentralized environments to enable fast and energy-efficient inter-device communication. However, this growing popularity also intensifies the need for robust security measures. While existing research has explored various aspects of FL security, the role of adversarial node placement in decentralized networks remains largely unexplored. This paper addresses this gap by analyzing the performance of decentralized FL for various adversarial placement strategies when adversaries can jointly coordinate their placement within a network. We establish two baseline strategies for placing adversarial node: random placement and network centrality-based placement. Building on this foundation, we propose a novel attack algorithm that prioritizes adversarial spread over adversarial centrality by maximizing the average network distance between adversaries. We show that the new attack algorithm significantly impacts key performance metrics such as testing accuracy, outperforming the baseline frameworks by between 9% and 66.5% for the considered setups. Our findings provide valuable insights into the vulnerabilities of decentralized FL systems, setting the stage for future research aimed at developing more secure and robust decentralized FL frameworks.

We study reliable communication over point-to-point adversarial channels in which the adversary can observe the transmitted codeword via some function that takes the$n$-bit codeword as input and computes an$rn$-bit output for some given$r \in [{0,1}]$. We consider the scenario where the$rn$-bit observation is computationally bounded – the adversary is free to choose an arbitrary observation function as long as the function can be computed using a polynomial amount of computational resources. This observation-based restriction differs from conventional channel-based computational limitations, where in the later case, the resource limitation applies to the computation of the (adversarial) channel error/corruption. For all$r \in [0,1-H(p)]$where$H(\cdot)$is the binary entropy function and$p$is the adversary’s error budget, we characterize the capacity of the above channel and find that the capacity is identical to the completely oblivious setting ($r=0$). This result can be viewed as a generalization of known results on myopic adversaries and on channels with active eavesdroppers for which the observation process depends on a fixed distribution and fixed-linear structure, respectively, that cannot be chosen arbitrarily by the adversary.

We consider error-correction coding schemes for adversarial wiretap channels (AWTCs) in which the channel can a) read a fraction of the codeword bits up to a bound r and b) flip a fraction of the bits up to a bound p. The channel can freely choose the locations of the bit reads and bit flips via a process with unbounded computational power. Codes for the AWTC are of broad interest in the area of information security, as they can provide data resiliency in settings where an attacker has limited access to a storage or transmission medium. We investigate a family of non-linear codes known as pseudolinear codes, which were first proposed by Guruswami and Indyk (FOCS 2001) for constructing list-decodable codes independent of the AWTC setting. Unlike general non-linear codes, pseudolinear codes admit efficient encoders and have succinct representations. We focus on unique decoding and show that random pseudolinear codes can achieve rates up to the binary symmetric channel (BSC) capacity $1-H_{2}(p)$ for any $p, r$ in the less noisy region: $p\lt1/2$ and $r\lt1-H_{2}(p)$ where $H_{2}(\cdot)$ is the binary entropy function. Thus, pseudolinear codes are the first known optimal-rate binary code family for the less noisy AWTC that admit efficient encoders. The above result can be viewed as a derandomization result of random general codes in the AWTC setting, which in turn opens new avenues for applying derandomization techniques to randomized constructions of AWTC codes. Our proof applies a novel concentration inequality for sums of random variables with limited independence which may be of interest as an analysis tool more generally.

A point-to-point communication is considered where a roadside unite (RSU) wishes to simultaneously send messages of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services to a vehicle. The eMBB message arrives at the beginning of a block and its transmission lasts over the entire block. During each eMBB transmission block, random arrivals of URLLC messages are assumed. To improve the reliability of the URLLC transmissions, the RSU reinforces their transmissions by mitigating the interference of eMBB transmission by means of dirty paper coding (DPC). In the proposed coding scheme, the eMBB messages are decoded based on two approaches: treating interference as noise, and successive interference cancellation. Rigorous bounds are derived for the error probabilities of eMBB and URLLC transmissions achieved by our scheme. Numerical results illustrate that they are lower than bounds for standard time-sharing.

We consider point-to-point communication with partial noiseless feedback in which the number of feedback bits is $O(1)$ in the number of transmitted symbols. For $q \geq 2$, we study the general q-ary alphabet setting with both errors and erasures and seek to characterize the zero-error capacity. As our main result, we provide a tight characterization of zero-error capacity which we prove via novel achievability and converse schemes inspired by the study of causal/online adversarial channels without feedback. Perhaps surprisingly, we show that $O(1)$-bits of feedback are sufficient to achieve the zero-error capacity of the error channel with full noiseless feedback when the fraction of transmitted symbols in error is sufficiently small.

Network sharing is widely accepted as a cost effective approach for mobile network deployment. It remains uncertain, however, how regulators will evaluate network sharing agreements (NSA) for future networks in the context of the current competition law. For example, 5G mobile network operators (MNOs) seeking to enter NSAs may risk legal challenges, as regulators have not given MNOs sufficient guidance for self-evaluation of their NSAs. One way for MNOs to reduce the risk of legal challenge is to avoid sharing variable costs in the NSA. However, constraining costs to be non-variable (i.e., fixed) rules out the use of most pricing mechanisms that have been widely adopted for dynamic resource trading between MNOs. In this article, we propose a network sharing framework to allow dynamic resource sharing without the use of resource pricing. To incentivize sharing without pricing, our framework presents sharing as a means for MNOs to differentiate services and better compete in the service market for profit. We evaluate our framework in a duopoly market model and demonstrate the economic and regulatory viability of our framework.

Cooperation between network nodes is critical for supporting services in ad hoc networks. Cooperation, however, is an idealized assumption that may not always be present. This assumption can fail because of moral hazard, a scenario in part caused by misaligned incentives between the requesting node and supporting node. In this paper, we characterize a moral hazard that perversely incentivizes nodes to increase their routing payments by transmitting interference into the multi-hop network. We refer to this as the interference moral hazard (IMH) problem which is inherent to strategyproof mechanisms with low overpayments. We investigate IMH as a non-cooperative game played by network nodes on a random graph. For large networks, we show that IMH can be solved in the network design space. We provide sufficient conditions on the network distribution that guarantee an equilibrium path with interference-free play. This is achieved by 1) lower-bounding the number of nodes and 2) bounding the network density slightly above the 2-connectedness threshold and below a proposed upper-bound. Simulations suggest that density plays a fundamental role in IMH.

We study reliable communication over point-to-point adversarial channels in which the adversary can observe the transmitted codeword via some function that takes the n-bit codeword as input and computes an rn-bit output for some given r ∈ [0,1]. We consider the scenario where the rn-bit observation is computationally bounded – the adversary is free to choose an arbitrary observation function as long as the function can be computed using a polynomial amount of computational resources. This observation-based restriction differs from conventional channel-based computational limitations, where in the later case, the resource limitation applies to the computation of the (adversarial) channel error. For all r ∈ [0,1 − H(p)] where H(•) is the binary entropy function and p is the adversary’s error budget, we characterize the capacity of the above channel. For this range of r, we find that the capacity is identical to the completely obvious setting (r = 0). This result can be viewed as a generalization of known results on myopic adversaries and channels with active eavesdroppers for which the observation process depends on a fixed distribution and fixed-linear structure, respectively, that cannot be chosen arbitrarily by the adversary.

The growing need for reliable communication over untrusted networks has caused a renewed interest in adversarial channel models, which often behave much differently than traditional stochastic channel models. Of particular practical use is the assumption of a causal or online adversary who is limited to causal knowledge of the transmitted codeword. In this work, we consider stochastic-adversarial mixed noise models. In the setup considered, a transmit node (Alice) attempts to communicate with a receive node (Bob) over a binary erasure channel (BEC) or binary symmetric channel (BSC) in the presence of an online adversary (Calvin) who can erase or flip up to a certain number of bits at the input of the channel. Calvin knows the encoding scheme and has strict causal access to Bob's reception through feedback snooping. For erasures, we provide a complete capacity characterization with and without transmitter feedback. For bit-flips, we provide converse and achievability bounds.