Final Technical Report: Randomized Federated Learning Methods for Nonsmooth, Nonconvex, and Hierarchical Optimization

Technical Report · Tue Dec 30 00:00:00 EST 2025

DOI:https://doi.org/10.2172/3011291· OSTI ID:3011291

Yousefian, Farzad; Shanbhag, Uday V

This final technical report summarizes the outcomes of a DOE-funded project on federated scientific machine learning (FL) under nonsmooth, nonconvex, and hierarchical optimization settings. The project develops new mathematical models, algorithms, and theoretical guarantees for decentralized stochastic, bilevel, and minimax optimization problems arising in DOE mission-relevant applications. A unified framework of randomized and zeroth-order federated optimization methods is introduced, providing provable convergence, communication efficiency, and sample-complexity guarantees. The report documents algorithmic design, theoretical analysis, and empirical validation of the proposed federated learning methods. The project also contributes to workforce development through graduate training and dissemination of results via publications and seminars.

• Research Organization: Rutgers, The State University; University of Michigan
• Sponsoring Organization: USDOE
• DOE Contract Number: SC0023303
• OSTI ID: 3011291
• Report Number(s): DOE-Rutgers-23303
• Country of Publication: United States
• Language: English