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Byzantine-Resilient Zero-Order Optimization for Communication-Efficient Heterogeneous Federated Learning
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We introduce CyBeR-0, a Byzantine-resilient federated zero-order optimization method that is robust under Byzantine attacks and provides significant savings in uplink and downlink communication costs. We introduce transformed robust aggregation to give convergence guarantees for general non-convex objectives under client data heterogeneity. Empirical evaluations for standard learning tasks and fine-tuning large language models show that CyBeR-0 exhibits stable performance with only a few scalars per-round communication cost and reduced memory requirements.
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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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