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Communication-Efficient Byzantine-Resilient Federated Zero-Order Optimization
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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.
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Private Aggregation for Byzantine-Resilient Heterogeneous Federated Learning
Nearest neighbor mixing can be composed with secure aggregation and private information retrieval to give information-theoretic privacy and Byzantine resilience for heterogeneous federated learning.
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