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Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation
Asynchronous Federated Learning with Buffered Aggregation (FedBuff) is a state-of-the-art algorithm known for its efficiency and high scalability.
Gossiped and Quantized Online Multi-Kernel Learning
In instances of online kernel learning where little prior information is available and centralized learning is unfeasible, past research has shown that distributed and online multi-kernel learning provides sub-linear regret as long as every pair of nodes in the network can communicate (i. e., the communications network is a complete graph).
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