EE Colloquium: Collaborative Machine Learning: A Pluralistic Approach with Provable Guarantees

Abstract: With the massive amount of data generated by the proliferation of mobile devices and internet of things (IoT), coupled with concerns over sharing private information, collaborative machine learning and the use of federated optimization is often crucial for deployment of large-scale machine learning.  Despite recent progress on federated optimization, our understanding of some fundamental aspects of these methods, required for characterizing their performance guarantees, is still in its infancy. In this talk we will introduce a pluralistic heterogeneous distributed optimization framework for collaborative machine learning where the main idea is to integrate the personalization into the training. We also discuss novel stochastic communication-efficient distributed algorithms with provable convergence rates for adaptively exploiting underlying computational resources and overcoming the curse of data heterogeneity. Finally, we establish generalization bounds for the proposed algorithm and elaborate on different implications such as intensive compatibility and game theoretic implications. 

Biography: Mehrdad Mahdavi has been an assistant professor of Computer Science and Engineering at EECS since 2018. His primary research lies at the interface of machine learning and optimization with a focus on developing theoretically principled and practically efficient algorithms for learning from massive datasets and complex domains.  He has won several awards including Top Cited Paper Award from the Journal of Applied Mathematics and Computation (Elsevier) in 2010 and the Mark Fulk Best Student Paper Award at the Conference on Learning Theory (COLT) in 2012. 


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Media Contact: Minghui Zhu



The School of Electrical Engineering and Computer Science was created in the spring of 2015 to allow greater access to courses offered by both departments for undergraduate and graduate students in exciting collaborative research fields.

We offer B.S. degrees in electrical engineering, computer science, computer engineering and data science and graduate degrees (master's degrees and Ph.D.'s) in electrical engineering and computer science and engineering. EECS focuses on the convergence of technologies and disciplines to meet today’s industrial demands.

School of Electrical Engineering and Computer Science

The Pennsylvania State University

207 Electrical Engineering West

University Park, PA 16802


Department of Computer Science and Engineering


Department of Electrical Engineering