AI RESEARCH
Subspace Optimization for Efficient Federated Learning under Heterogeneous Data
arXiv CS.LG
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ArXi:2604.25467v1 Announce Type: new Federated learning increasingly operates in a large-model regime where communication, memory, and computation are all scarce. Typically, non-IID client data induce drift that degrades the stability and performance of local