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Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning
The centered clipping (CC) framework has further shown that, the momentum term from the previous iteration, besides reducing the variance, can be used as a reference point to neutralize Byzantine attacks better.
Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs
As wireless standards evolve, more complex functionalities are introduced to address the increasing requirements in terms of throughput, latency, security, and efficiency.
Less is More: Feature Selection for Adversarial Robustness with Compressive Counter-Adversarial Attacks
Hence, we propose a novel approach to identify the important features by employing counter-adversarial attacks, which highlights the consistency at the penultimate layer with respect to perturbations on input samples.
Time-Correlated Sparsification for Communication-Efficient Federated Learning
Sparse communication is often employed to reduce the communication load, where only a small subset of the model updates are communicated from the clients to the PS.
FedADC: Accelerated Federated Learning with Drift Control
The core of the FL strategy is the use of stochastic gradient descent (SGD) in a distributed manner.
Distributed Sparse SGD with Majority Voting
However, top-K sparsification requires additional communication load to represent the sparsity pattern, and the mismatch between the sparsity patterns of the workers prevents exploitation of efficient communication protocols.
