I am an Assistant Professor in the Department of Electrical and Computer Engineering at University of California, Riverside, and a cooperating faculty at the Department of Computer Science and Engineering.
Our research is on information theory, distributed/federated learning, and trustworthy AI/ML over large-scale networks. We design massive-scale reliable networked information systems with strong information-theoretic guarantees for security, privacy, and sustainability.
Prospective students please follow this link.
Contact: I can be reached at bguler at ece dot ucr dot edu
Sep 2023: Xingyu and Hasin had their paper accepted to IEEE Transactions on Communications, congratulations!
Aug 2023: Yushu had his first paper accepted to the Asilomar Conference!
July 2023: New collaborative funding award for AI Equity and Sustainability!
June 2023: Xingyu and Umit presented their paper at FL-ICML Workshop on Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities, congratulations!
May 2023: Serving as a TPC member for INFOCOM 2024!
May 2023: Google Cloud Research Innovator!
April 2023: Our group has two papers accepted at ISIT 2023, both strong accepts! Congratulations to Hasin and Xingyu!
March 2023: Organizing a special session on federated learning and wireless edge intelligence at Asilomar 2023!
January 2023: New paper accepted to AISTATS 2023, congratulations Xingyu and Hasin!
December 2022: Our paper Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning has been accepted to AAAI 2023!
September 2022: Our group has received a UCR OASIS Funding Award to support our research.
August 2022: Serving as a track co-chair for ML and Optimization for Wireless Systems at WCNC 2023, submission deadline Sept 12, 2022!
August 2022: Our paper Communication-Efficient Secure Aggregation for Federated Learning has been accepted to Globecom 2022.
July 2022: Serving as a panelist at the North American School of Information Theory.
June 2022: Organizing an AI workshop for high school students at UC Riverside in July 2022, in tion with the Redlands Unified School District.
Apr 2022: Seminar at UCLA.
Feb 2022: Received an NSF CAREER Award!
Jan 2022: Our paper Over-the Air Clustered Federated Learning has been accepted to International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2022). Congratulations Hasin!
Dec 2021: New paper on privacy-preserving federated learning! at ICLR 2022 ICLR Workshop on Socially Responsible Machine Learning. We introduce a sparse communication framework for speeding up secure aggregation.
Oct 2021: New paper on asynchronous federated learning! Preliminary version to appear at NeurIPS Workshop on Federated Learning: New Challenges on Privacy, Fairness, Robustness, Personalization and Data Ownership.
Sep 2021: Serving as a TPC member for IEEE International Symposium on Information Theory (ISIT 2022), submission deadline Jan 15, 2022!
Aug 2021: Serving as a TPC member for IEEE Wireless Communications and Networking Conference (WCNC 2022), submission deadline Oct 15, 2022!
July 2021: Received the UCR Regents' Faculty Fellowship!
July 2021: New paper on sustainable/green federated learning A Framework for Sustainable Federated Learning at the International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt)!
June 2021: New paper on privacy-preserving federated learning! Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning, preliminary version accepted at FL-AAAI Workshop 2022.
May 2021: Research highlight from the ECE Department! Privacy-Aware Large-Scale Machine Learning
April 2021: Our paper on sustainable/green machine learning Energy Harvesting Distributed Machine Learning has been accepted to IEEE International Symposium on Information Theory!
January 2021: Our paper Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning has been accepted to IEEE Journal on Selected Areas in Information Theory: Privacy and Security of Information Systems
January 2021: Our paper CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning has been accepted to IEEE Journal on Selected Areas in Information Theory: Privacy and Security of Information Systems
October 2020: Our paper BREA: Byzantine-Resilient Secure Federated Learning has been accepted to IEEE Journal on Selected Areas in Communications: Machine Learning in Communications and Networks!
October 2020: New project on resilient communications and computing in large-scale networks!
September 2020: Our paper A Scalable Approach for Privacy-Preserving Collaborative Machine-Learning has been accepted to Conference on Neural Information Processing Systems (NeurIPS 2020)!
July 2020: New paper on Byzantine-robust privacy-preserving federated learning: BREA: Byzantine-Resilient Secure Federated Learning
May 2020: New paper on communication-efficient secure federated learning: Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning
May 2020: New paper on distributed graph processing: TACC: Topology-Aware Coded Computing for Distributed Graph Processing
April 2020: New paper on privacy-aware distributed machine learning: CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning
Our research is supported by a diverse range of projects. We acknowledge all our past and current sponsors.