DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization

no code implementations • 5 Dec 2022 • Peiwen Qiu, Yining Li, Zhuqing Liu, Prashant Khanduri, Jia Liu, Ness B. Shroff, Elizabeth Serena Bentley, Kurt Turck

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-learning and multi-agent reinforcement learning) over peer-to-peer edge networks.

Bilevel Optimization Meta-Learning +1

Taming Fat-Tailed ("Heavier-Tailed'' with Potentially Infinite Variance) Noise in Federated Learning

no code implementations • 3 Oct 2022 • Haibo Yang, Peiwen Qiu, Jia Liu

A key assumption in most existing works on FL algorithms' convergence analysis is that the noise in stochastic first-order information has a finite variance.

Federated Learning

Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control

no code implementations • 12 May 2022 • Haibo Yang, Peiwen Qiu, Jia Liu, Aylin Yener

In order to fully utilize this advantage while providing comparable learning performance to conventional federated learning that presumes model aggregation via noiseless channels, we consider the joint design of transmission scaling and the number of local iterations at each round, given the power constraint at each edge device.

Federated Learning