학술논문

Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimization
Document Type
Working Paper
Source
Subject
Computer Science - Machine Learning
Computer Science - Artificial Intelligence
Language
Abstract
We introduce CYBER-0, the first zero-order optimization algorithm for memory-and-communication efficient Federated Learning, resilient to Byzantine faults. We show through extensive numerical experiments on the MNIST dataset and finetuning RoBERTa-Large that CYBER-0 outperforms state-of-the-art algorithms in terms of communication and memory efficiency while reaching similar accuracy. We provide theoretical guarantees on its convergence for convex loss functions.