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Reconciling Communication Compression and Byzantine-Robustness in Distributed Learning

arXiv — cs.LG•Tuesday, November 4, 2025 at 5:00:00 AM
NeutralArtificial Intelligence
A recent study highlights the challenges of combining communication compression with Byzantine-robustness in distributed learning. While distributed learning allows for efficient model training across decentralized data, it faces issues from Byzantine faults and high communication costs. This research sheds light on how these two challenges interact, revealing that simply merging techniques can compromise the system's resilience to faulty nodes. Understanding this relationship is crucial for improving the reliability and efficiency of distributed learning systems.
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