FedFIbOS: Fisher Importance based Optimal Submodelling for Heterogeneous Federated Learning
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arXiv:2609.19559v1 Announce Type: new Abstract: Heterogeneous federated learning requires clients with diverse computational capacities to collaboratively train a global model, where each client trains a capacity-constrained submodel. Existing methods select submodel parameters using heuristic impo…
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- 2026-09-18 04:00 · arXiv cs.LG
FedFIbOS: Fisher Importance based Optimal Submodelling for Heterogeneous Federated Learning