AI RESEARCH
FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection
arXiv CS.LG
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ArXi:2604.24012v1 Announce Type: new Federated learning enables a population of clients to collaboratively train machine learning models without exchanging their raw data, but standard algorithms such as FedAvg suffer from slow convergence and high communication and memory costs in heterogeneous, resource-constrained environments. We