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cs.LG2022
Distributed Non-Convex Optimization with One-Bit Compressors on Heterogeneous Data: Efficient and Resilient Algorithms
Ming Xiang, Lili Su
Federated Learning (FL) is a nascent decentralized learning framework under which a massive collection of heterogeneous clients collaboratively train a model without revealing thei…
cs.LG2022
Global Convergence of Federated Learning for Mixed Regression
Lili Su, Jiaming Xu, Pengkun Yang
This paper studies the problem of model training under Federated Learning when clients exhibit cluster structure. We contextualize this problem in mixed regression, where each clie…
cs.NI2022
Experimental Design Networks: A Paradigm for Serving Heterogeneous Learners under Networking Constraints
Yuezhou Liu, Yuanyuan Li, Lili Su +2
Significant advances in edge computing capabilities enable learning to occur at geographically diverse locations. In general, the training data needed in those learning tasks are n…