26 citations · 60 across the 11 of their papers we have counts for
6 papers · 1 filter
Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation
Haibo Yang, Peiwen Qiu, Prashant Khanduri +2
Existing works in federated learning (FL) often assume an ideal system with either full client or uniformly distributed client participation. However, in practice, it has been obse…
An Introduction to Bi-level Optimization: Foundations and Applications in Signal Processing and Machine Learning
Yihua Zhang, Prashant Khanduri, Ioannis Tsaknakis +3
Recently, bi-level optimization (BLO) has taken center stage in some very exciting developments in the area of signal processing (SP) and machine learning (ML). Roughly speaking, B…
DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization
Peiwen Qiu, Yining Li, Zhuqing Liu +5
Decentralized bilevel optimization has received increasing attention recently due to its foundational role in many emerging multi-agent learning paradigms (e.g., multi-agent meta-l…
INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks
Zhuqing Liu, Xin Zhang, Prashant Khanduri +2
In recent years, decentralized bilevel optimization problems have received increasing attention in the networking and machine learning communities thanks to their versatility in mo…
Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization
Yihua Zhang, Guanhua Zhang, Prashant Khanduri +3
Adversarial training (AT) is a widely recognized defense mechanism to gain the robustness of deep neural networks against adversarial attacks. It is built on min-max optimization (…
Anarchic Federated Learning
Haibo Yang, Xin Zhang, Prashant Khanduri +1
Present-day federated learning (FL) systems deployed over edge networks consists of a large number of workers with high degrees of heterogeneity in data and/or computing capabiliti…