Federated learning catastrophically fails at 5-second latency, over 50% packet loss, and 90% client dropout due to TCP mismatches, but tuning three TCP parameters reduces training time under extreme conditions.
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Threshold Modulation dynamically adjusts firing thresholds in SNNs via neuronal dynamics-inspired normalization to enable online test-time adaptation under distribution shifts.
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Surviving the Edge: Federated Learning under Networking and Resource Constraints
Federated learning catastrophically fails at 5-second latency, over 50% packet loss, and 90% client dropout due to TCP mismatches, but tuning three TCP parameters reduces training time under extreme conditions.
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Threshold Modulation for Online Test-Time Adaptation of Spiking Neural Networks
Threshold Modulation dynamically adjusts firing thresholds in SNNs via neuronal dynamics-inspired normalization to enable online test-time adaptation under distribution shifts.