A co-distillation federated learning variant sharing majority-class feature averages is reported to keep minority-class accuracy higher than FedAvg, FedProto, FedAMP, and FedDistill on two medical imaging datasets under class skew.
Learn Electronic Health Records by Fully Decentralized Federated Learning
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abstract
Federated learning opens a number of research opportunities due to its high communication efficiency in distributed training problems within a star network. In this paper, we focus on improving the communication efficiency for fully decentralized federated learning over a graph, where the algorithm performs local updates for several iterations and then enables communications among the nodes. In such a way, the communication rounds of exchanging the common interest of parameters can be saved significantly without loss of optimality of the solutions. Multiple numerical simulations based on large, real-world electronic health record databases showcase the superiority of the decentralized federated learning compared with classic methods.
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Framework for Co-distillation Driven Federated Learning to Address Class Imbalance in Healthcare
A co-distillation federated learning variant sharing majority-class feature averages is reported to keep minority-class accuracy higher than FedAvg, FedProto, FedAMP, and FedDistill on two medical imaging datasets under class skew.