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Found 3 papers, 2 papers with code
Decentralized Sporadic Federated Learning: A Unified Methodology with Generalized Convergence Guarantees
Decentralized Federated Learning (DFL) has received significant recent research attention, capturing settings where both model updates and model aggregations -- the two key FL processes -- are conducted by the clients.
Event-Triggered Decentralized Federated Learning over Resource-Constrained Edge Devices
We theoretically demonstrate that our methodology converges to the globally optimal learning model at a $O{(\frac{\ln{k}}{\sqrt{k}})}$ rate under standard assumptions in distributed learning and consensus literature.
Decentralized Event-Triggered Federated Learning with Heterogeneous Communication Thresholds
Through theoretical analysis, we demonstrate that our methodology achieves asymptotic convergence to the globally optimal learning model under standard assumptions in distributed learning and graph consensus literature, and without restrictive connectivity requirements on the underlying topology.
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