Federated Learning via Plurality Vote

2021/10/06 by Kai Yue, Yue, Kai, Richeng Jin +5 · 1 citation
Computer Science · Engineering · #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #Distributed #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Parallel #Privacy-Preserving Technologies in Data #Signal Processing (eess.SP) #Stochastic Gradient Optimization Techniques #and Cluster Computing (cs.DC) #cs.AI #cs.DC #cs.LG #eess.SP #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2110.02998

openalex publication_date 2021/10/06 · arxiv created 2022/12/10 · arxiv updated 2022/12/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Binarized Neural Networks
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms
• Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging
• Learning Multiple Layers of Features from Tiny Images

Cited by

• Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontiers

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