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%252C+resilient+decentralized+learning+algorithms+are+able+to+limit+the+impact+of+these+Byzantine+agents+without+knowing+their+number+and+identities%252C+and+have+guaranteed+optimization+errors.+However%252C+analysis+of+the+generalization+errors%252C+which+are+critical+to+implementations+of+the+trained+models%252C+is+still+lacking.+In+this+paper%252C+we+provide+the+first+analysis+of+the+generalization+errors+for+a+class+of+popular+Byzantine-resilient+decentralized+stochastic+gradient+descent+(DSGD)+algorithms.+Our+theoretical+results+reveal+that+the+generalization+errors+cannot+be+entirely+eliminated+because+of+the+presence+of+the+Byzantine+agents%252C+even+if+the+number+of+training+samples+are+infinitely+large.+Numerical+experiments+are+conducted+to+confirm+our+theoretical+results.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.abba2b492e1af2ea8ff7e58d139a56a7%3frik%3dE5emh%252bwFzt77XQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Generalization+Error+in+Machine+Learning&FORM=IRPRST&ck=9C5124B53F99D2328B0E440FD4318FCE&selectedIndex=6&itb=0)
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