Communication-Efficient Robust Federated Learning Over Heterogeneous Datasets

Understand

This work investigates fault-resilient federated learning when the data samples are non-uniformly distributed across workers, and the number of faulty workers is unknown to the central server.

• In the presence of adversarially faulty workers who may strategically corrupt datasets, the local messages exchanged (e.g., local gradients and/or local model parameters) can be unreliable, and thus the vanilla stochastic gradient descent (SGD) algorithm is not guaranteed to converge.
• Recently developed algorithms improve upon vanilla SGD by providing robustness to faulty workers at the price of slowing down convergence.
• To remedy this limitation, the present work introduces a fault-resilient proximal gradient (FRPG) algorithm that relies on Nesterov's acceleration technique.