RESIST achieves algorithmic and statistical convergence guarantees for strongly convex, PL, and nonconvex ERM under MITM attacks via multistep consensus gradient descent plus robust screening.
Learning multiple layers of features from tiny images
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An interpolation approximation plus layer-weighted loss enables stronger data reconstruction attacks on FedAvg federated learning, with experiments showing gains on image data over prior methods.
Threshold Modulation dynamically adjusts firing thresholds in SNNs via neuronal dynamics-inspired normalization to enable online test-time adaptation under distribution shifts.
citing papers explorer
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RESIST: Resilient Decentralized Learning Using Consensus Gradient Descent
RESIST achieves algorithmic and statistical convergence guarantees for strongly convex, PL, and nonconvex ERM under MITM attacks via multistep consensus gradient descent plus robust screening.
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Approximate and Weighted Data Reconstruction Attack in Federated Learning
An interpolation approximation plus layer-weighted loss enables stronger data reconstruction attacks on FedAvg federated learning, with experiments showing gains on image data over prior methods.
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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.