Past Events

PhD Defense

Multi-Dimensional Federated Learning In Recommender Systems


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Thursday, September 15, 2022, 09:00am - 11:00am


Speaker: Shuchang Liu

Location : Virtual


Professor Amelie Marian (Chair)

Professor  Yongfeng Zhang (Co-advisor)

Professor Hao Wang

Professor Qingyao Ai (University of Utah)

Event Type: PhD Defense

Abstract: A wide range of web services like e-commerce, job-searching, and target advertising heavily rely on recommender systems that finds products of interest to fulfills users' diverse and complicated demands. To better model the user preferences and provide satisfactory recommendations, there has been an increasing amount of research focusing on constructing more accurate and complete user representations that exploit the user profile and behavior history. Inevitably, this motion would induce privacy risks for users. This natural conflict between user privacy and recommendation accuracy has drawn lots of attention in recent years. Among these solutions, the most widely studied and verified method is the federated learning techniques. The general idea behind federated learning is maintaining users' critical data on the edge devices (e.g. mobile phones) which communicate only the model parameters to the central server.However, the standard federated learning system is designed for a single task with a simple learning objective. In reality, a user typically interacts with various applications everyday with heterogeneous intentions. In this defense, I will discuss the extended federated learning in a more complex but practical multi-objective setting, where multiple federated learning agents collaborate in an environment with multiple central servers and a number of distributed edge devices. Formally, this machine learning problem is regarded as a multi-objective federated optimization problem, and I identify several main challenges including the dimensional heterogeneity, conflicting objectives, multi-dimensional communication overhead. Then, I will illustrate the general solution framework and illustrate several techniques that could solve the aforementioned challenges.


Department of Computer Science

School of Arts & Sciences

Rutgers University

Contact  Professor Amelie Marian

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