4 papers
Federated learning with heavy-tailed gradient noise and communication noise: a variance-reduction based algorithm
Shengchao Zhao, Yongchao Liu
Federated learning (FL) is an emerging distributed machine learning paradigm that enables local devices to jointly train a global model while keeping data decentralized and private…
Distributed TD Tracking with Linear Function Approximation over Directed Communication Networks
Haocheng Yang, Shengchao Zhao, Yongchao Liu
We study the policy evaluation problem in multi-agent reinforcement learning (MARL) over directed communication networks, where agents cooperate with each other to explore an unkno…
Investigating Group Relative Policy Optimization for Diffusion Transformer based Text-to-Audio Generation
Yi Gu, Yanqing Liu, Chen Yang +1
Text-to-audio (T2A) generation has advanced considerably in recent years, yet existing methods continue to face challenges in accurately rendering complex text prompts, particularl…
Efficient Gradient Tracking Algorithms for Distributed Optimization Problems with Inexact Communication
Shengchao Zhao, Yongchao Liu
Distributed optimization problems usually face inexact communication issues induced by channel noise, communication quantization or differential privacy protection. Most existing a…