FedFit: Server Aggregation Through Linear Regression in Federated Learning
Taiga Kashima,
Ikki Kishida,
Ayako Amma
et al.
Abstract:We present a conceptually novel framework for Federated Learning (FL) called FedFit for a flexible solver to address FL problems. FedFit framework consists of two components: model compression to upload a local model from a client to the server and reconstruction of the compressed local model in the server. Clients upload a compressed local model using a ''key'' shared with the server to formulate the server aggregation as linear regression. Therefore, the global model's parameters are updated through a linear… Show more
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