  Federated learning allows collaborative workers to solve a machine learning
problem while preserving data privacy. Recent studies have tackled various
challenges in federated learning, but the joint optimization of communication
overhead, learning reliability, and deployment efficiency is still an open
problem. To this end, we propose a new scheme named federated learning via
plurality vote (FedVote). In each communication round of FedVote, workers
transmit binary or ternary weights to the server with low communication
overhead. The model parameters are aggregated via weighted voting to enhance
the resilience against Byzantine attacks. When deployed for inference, the
model with binary or ternary weights is resource-friendly to edge devices. We
show that our proposed method can reduce quantization error and converges
faster compared with the methods directly quantizing the model updates.
