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This paper investigates the impact of differential privacy (DP) mechanisms, namely gradient clipping and noise injection, on firing rate statistics within federated spiking neural networks (SNNs). The study demonstrates that DP significantly perturbs firing rates, leading to rate shifts, attenuated aggregation, and unstable client selection in a speech recognition task under non-IID data. The authors further link these rate shifts to sparsity and memory usage, providing insights into the trade-offs between privacy and performance in rate-based federated neuromorphic learning.
Differential Privacy can wreak havoc on the delicate firing rate dynamics of federated spiking neural networks, leading to instability in client selection and attenuated aggregation.
Federated Neuromorphic Learning (FNL) enables energy-efficient and privacy-preserving learning on devices without centralizing data. However, real-world deployments require additional privacy mechanisms that can significantly alter training signals. This paper analyzes how Differential Privacy (DP) mechanisms, specifically gradient clipping and noise injection, perturb firing-rate statistics in Spiking Neural Networks (SNNs) and how these perturbations are propagated to rate-based FNL coordination. On a speech recognition task under non-IID settings, ablations across privacy budgets and clipping bounds reveal systematic rate shifts, attenuated aggregation, and ranking instability during client selection. Moreover, we relate these shifts to sparsity and memory indicators. Our findings provide actionable guidance for privacy-preserving FNL, specifically regarding the balance between privacy strength and rate-dependent coordination.
