The rise of Edge AI necessitates energy-efficient models like Spiking Neural Networks (SNNs), often trained using Federated Learning (FL) to preserve data privacy. However, FL is vulnerable to Byzantine attacks, where malicious clients disrupt training. While SNNs offer potential energy benefits due to their event-driven nature, their unique training mechanisms, particularly the use of surrogate gradients to handle non-differentiable spike events, raise questions about their inherent robustness in adversarial FL settings. We evaluate the robustness of SNNs employing 5 surrogate gradients (distinct by function shape) against 7 diverse Byzantine attacks and assess recovery potential using 5 robust aggregation rules (AGRs). Our extensive experiments (1032 runs) reveal that SNNs are not universally more robust than ANNs; they show resilience to certain structured attacks (e.g., MinMax) but vulnerability to others (e.g., Label Flip). We find a moderate positive correlation between surrogate gradient choice and recovery effectiveness using AGRs, with Triangle and Rectangle surrogates often enabling better recovery, though this advantage is context-dependent. Our results underscore that robust AGRs (like DnC and RFA) are essential for mitigating attacks in SNN-based FL, regardless of the surrogate gradient used. We conclude that achieving reliable SNN deployment in adversarial FL requires a holistic, context-aware approach, carefully considering the interplay between network type, surrogate gradient, threat model, and defense mechanisms. Our code is open-sourced for reproducibility1.

Spiking Neural Networks (SNN), which offer exceptional energy efficiency for inference, and Federated Learning (FL), which offers privacy-preserving training, is a rising area of interest that highly beneficial towards Internet of Things (IoT) devices. Despite this, research that tackles Byzantine attacks and bandwidth limitation in FL-SNN, both poses significant threats on model convergence and training times, still remains largely unexplored. In this paper, we first systematically evaluate the robustness of ANN and SNN in the FL context under four model-poisoning Byzantine attacks. We find that FL-SNN demonstrate better reliability than FL-ANN against most Byzantine attacks except MinMax. We then propose the$Top-\kappa$sparsification approach for better robustness in FL-SNN and to reduce communication overhead. Using this simple compression method, we observe ~40% accuracy enhancement in FL-SNN training under the lethal MinMax attack, leading to FL-SNN being more robust than FL-ANN in all four model-poisoning Byzantine attacks. This study highlights the dual benefits of FL-SNN with$Top-\kappa$sparsification in significantly reduce energy consumption and provide better robustness to Byzantine attacks for edge AI applications.

Spiking Neural Networks (SNNs) have recently gained significant interest in on-chip learning in embedded devices and emerged as an energy-efficient alternative to conventional Artificial Neural Networks (ANNs). However, to extend SNNs to a Federated Learning (FL) setting involving collaborative model training, the communication between the local devices and the remote server remains the bottleneck, which is often restricted and costly. In this paper, we first explore the inherent robustness of SNNs under noisy communication in FL. Building upon this foundation, we propose a novel Federated Learning with Top-κ Sparsification (FLTS) algorithm to reduce the bandwidth usage for FL training. We discover that the proposed scheme with SNNs allows more bandwidth savings compared to ANNs without impacting the model’s accuracy. Additionally, the number of parameters to be communicated can be reduced to as low as 6% of the size of the original model. We further improve the communication efficiency by enabling dynamic parameter compression during model training. Extensive experiment results demonstrate that our proposed algorithms significantly outperform the baselines in terms of communication cost and model accuracy and are promising for practical network-efficient FL with SNNs.

5G radio access network (RAN) with network slicing methodology plays a key role in the development of the next-generation network system. RAN slicing focuses on splitting the substrate’s resources into a set of self-contained programmable RAN slices. Leveraged by network function virtualization (NFV), a RAN slice is constituted by various virtual network functions (VNFs) and virtual links that are embedded as instances on substrate nodes. In this work, we focus on the following fundamental tasks: i) establishing the theoretical foundation for constructing a VNF mapping plan for RAN slice recovery optimization and ii) developing algorithms needed to map/embed VNFs efficiently. Specifically, we propose four efficient algorithms, including Resource-based Algorithm (RBA), Connectivity-based Algorithm (CBA), Group-based Algorithm (GBA), and Group-Connectivity-based Algorithm (GCBA) to solve the resource allocation and VNF mapping problem. Extensive experiments are also conducted to validate the robustness of RAN slicing via the proposed algorithms.

Quantum computing has paved a new way for faster and more efficient solutions to large-scale, real-world optimization problems that are challenging for classical computing systems. For instance, selective traveling salesman problem (sTSP) that is famous in such fields as logistic optimization and has attracted increasing attention from the research community, however, is known as an NP-Hard problem. Solving the sTSP is, therefore, extremely complex because the optimization function potentially comes with an exponential number of variables that cannot be solved in polynomial time in general. To this end, we propose a quantum annealing framework for time-bounded and near-optimal solutions for the sTSP, overcoming hardware limits of near-term quantum devices. In particular, we put forth an efficient Hamiltonian (QUBO) to encode the complex decision-making for the sTSP on noisy intermediate-scale quantum (NISQ) annealer. Furthermore, experimental results we obtained on the D-Wave 2000$Q$quantum hardware demonstrate that the optimal solutions for several instances can be attained.