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%252C+the+common+paradigm+that+FedAvg+proposes+and+most+algorithms+follow+is+that+clients+train+local+models+with+their+private+data%252C+and+the+model+parameters+are+shared+for+central+aggregation%252C+mostly+averaging.+In+this+paradigm%252C+the+communication+cost+is+often+a+challenge%252C+as+modern+massive+neural+networks+can+contain+millions+to+billions+parameters.+We+suggest+that+clients+do+not+share+model+parameters+but+local+data+summaries%252C+to+decrease+the+cost+of+sharing.+We+develop+a+new+algorithm+FedLog+with+Bayesian+inference%252C+which+shares+only+sufficient+statistics+of+local+data.+FedLog+transmits+messages+as+small+as+the+last+layer+of+the+original+model.+We+conducted+comprehensive+experiments+to+show+we+outperform+other+FL+algorithms+that+aim+at+decreasing+the+communication+cost.+To+provide+formal+privacy+guarantees%252C+we+further+extend+FedLog+with+differential+privacy+and+show+the+trade-off+between+privacy+budget+and+accuracy.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.c026e43a7126608c0c44d87df34e9417%3frik%3dk3z%252bv3HO6XcLRA%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=FedLog&FORM=IRPRST&ck=CCBF6B4BAD7907384F8FB5DD75E32103&selectedIndex=14&itb=0)
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