BEGIN:VCALENDAR VERSION:2.0 PRODID:-//UC Irvine//CML Seminars//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:Provably Personalized and Robust Federated Learning X-WR-TIMEZONE:America/Los_Angeles BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT DTSTART:20070311T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0700 TZOFFSETTO:-0800 TZNAME:PST DTSTART:20071104T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:2023-11-06-mariel-werner@cml.ics.uci.edu DTSTAMP:20231106T000000Z SEQUENCE:57690 DTSTART;TZID=America/Los_Angeles:20231106T130000 DTEND;TZID=America/Los_Angeles:20231106T140000 SUMMARY:[CML Seminar] Mariel Werner: Provably Personalized and Robust Feder ated Learning LOCATION:Donald Bren Hall 4011 DESCRIPTION:Mariel Werner\, PhD Student\, Department of Electrical Engineer ing and Computer Science\, UC Berkeley\n\nTitle: Provably Personalized and Robust Federated Learning\n\nAbstract: I will be discussing my recent wor k on personalization in federated learning. Federated learning is a powerf ul distributed optimization framework in which multiple clients collaborat ively train a global model without sharing their raw data. In this work\, we tackle the personalized version of the federated learning problem. In p articular\, we ask: throughout the training process\, can clients identify a subset of similar clients and collaboratively train with just those cli ents? In the affirmative\, we propose simple clustering-based methods whic h are provably optimal for a broad class of loss functions\, are robust to malicious attackers\, and perform well in practice.\n\nhttps://cml.ics.uc i.edu/seminars/2023-11-06-mariel-werner X-ALT-DESC;FMTTYPE=text/html:Mariel Werner\, PhD Student \, Department of Electrical Engineering and Computer Science\, UC Berkeley

Title: Provably Personalized and Robust Federated Learning< br>
Abstract: I will be discussing my recent work on personaliza tion in federated learning. Federated learning is a powerful distributed o ptimization framework in which multiple clients collaboratively train a gl obal model without sharing their raw data. In this work\, we tackle the pe rsonalized version of the federated learning problem. In particular\, we a sk: throughout the training process\, can clients identify a subset of sim ilar clients and collaboratively train with just those clients? In the aff irmative\, we propose simple clustering-based methods which are provably o ptimal for a broad class of loss functions\, are robust to malicious attac kers\, and perform well in practice.

https://cml.ics.uci.edu/seminars/202 3-11-06-mariel-werner URL:https://cml.ics.uci.edu/seminars/2023-11-06-mariel-werner END:VEVENT END:VCALENDAR