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%20mainly%20focus%20on%20sending%20model%20parameters%20or%20gradients%20from%20clients%20to%20a%20server.%20However%2C%20these%20methods%20are%20plagued%20by%20significant%20inefficiency%2C%20privacy%2C%20and%20security%20concerns.%20Thanks%20to%20the%20emerging%20foundation%20generative%20models%2C%20we%20propose%20a%20novel%20federated%20learning%20framework%2C%20namely%20Federated%20Generative%20Learning.%20In%20this%20framework%2C%20each%20client%20can%20create%20text%20embeddings%20that%20are%20tailored%20to%20their%20local%20data%2C%20and%20send%20embeddings%20to%20the%20server.%20Then%20the%20informative%20training%20data%20can%20be%20synthesized%20remotely%20on%20the%20server%20using%20foundation%20generative%20models%20with%20these%20embeddings%2C%20which%20can%20benefit%20FL%20tasks.%20Our%20proposed%20framework%20offers%20several%20advantages%2C%20including%20increased%20communication%20efficiency%2C%20robustness%20to%20data%20heterogeneity%2C%20substantial%20performance%20improvements%2C%20and%20enhanced%20privacy%20protection.%20We%20validate%20these%20benefits%20through%20extensive%20experiments%20conducted%20on%2012%20datasets.%20For%20example%2C%20on%20the%20ImageNet100%20dataset%20with%20a%20highly%20skewed%20data%20distribution%2C%20our%20method%20outperforms%20FedAvg%20by%2012%25%20in%20a%20single%20communication%20round%2C%20compared%20to%20FedAvg's%20performance%20over%20200%20communication%20rounds.%20We%20have%20released%20the%20code%20for%20all%20experiments%20conducted%20in%20this%20study.)
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