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+has+shown+promising+results+on+training+machine+learning+models+with+privacy+preservation.+However%252C+for+large+models+with+over+100+million+parameters%252C+the+training+resource+requirement+becomes+an+obstacle+for+FL+because+common+devices+do+not+have+enough+memory+and+computation+power+to+finish+the+FL+tasks.+Although+efficient+training+methods+have+been+proposed%252C+it+is+still+a+challenge+to+train+the+large+models+like+Conformer+based+ASR.+This+paper+presents+a+systematic+solution+to+train+the+full-size+ASR+models+of+130M+parameters+with+FL.+To+our+knowledge%252C+this+is+the+first+real-world+FL+application+of+the+Conformer+model%252C+which+is+also+the+largest+model+ever+trained+with+FL+so+far.+And+this+is+the+first+paper+showing+FL+can+improve+the+ASR+model+quality+with+a+set+of+proposed+methods+to+refine+the+quality+of+data+and+labels+of+clients.+We+demonstrate+both+the+training+efficiency+and+the+model+quality+improvement+in+real-world+experiments.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.5579a4199ca74d45c0077722a096ac94%3frik%3d2KaBlJBZ8PD6Vw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Diagrams+for+Federated+Learning+in+ASR&FORM=IRPRST&ck=CACEFBB4234D232FE35156A123B0B645&selectedIndex=1&itb=0)
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