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Gradient Generator
+mainly+focus+on+sending+model+parameters+or+gradients+from+clients+to+a+server.+However%252C+these+methods+are+plagued+by+significant+inefficiency%252C+privacy%252C+and+security+concerns.+Thanks+to+the+emerging+foundation+generative+models%252C+we+propose+a+novel+federated+learning+framework%252C+namely+Federated+Generative+Learning.+In+this+framework%252C+each+client+can+create+text+embeddings+that+are+tailored+to+their+local+data%252C+and+send+embeddings+to+the+server.+Then+the+informative+training+data+can+be+synthesized+remotely+on+the+server+using+foundation+generative+models+with+these+embeddings%252C+which+can+benefit+FL+tasks.+Our+proposed+framework+offers+several+advantages%252C+including+increased+communication+efficiency%252C+robustness+to+data+heterogeneity%252C+substantial+performance+improvements%252C+and+enhanced+privacy+protection.+We+validate+these+benefits+through+extensive+experiments+conducted+on+12+datasets.+For+example%252C+on+the+ImageNet100+dataset+with+a+highly+skewed+data+distribution%252C+our+method+outperforms+FedAvg+by+12%2525+in+a+single+communication+round%252C+compared+to+FedAvg%27s+performance+over+200+communication+rounds.+We+have+released+the+code+for+all+experiments+conducted+in+this+study.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.f14c6f08e77d4b6ba9e31f1361f6a82f%3frik%3dydgTC7efb8kGZg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Federated+Learning+Gradient+Generator&FORM=IRPRST&ck=9DB7FB73C88BD44EBFAD608C116BDCAE&selectedIndex=27&itb=0)
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