Qing Ling

BROADCAST: Reducing Both Stochastic and Compression Noise to Robustify Communication-Efficient Federated Learning

2021 · arXiv (Cornell University)

Communication between workers and the master node to collect local stochastic gradients is a key bottleneck in a large-scale federated learning system. Various recent works have proposed to compress the local stochastic gradients to mitigate …

Variance-Reduced Stochastic Quasi-Newton Methods for Decentralized Learning: Part II

2022 · arXiv (Cornell University)

In Part I of this work, we have proposed a general framework of decentralized stochastic quasi-Newton methods, which converge linearly to the optimal solution under the assumption that the local Hessian inverse approximations have bounded …

Byzantine-Robust Federated Deep Deterministic Policy Gradient

2022 · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Federated reinforcement learning (FRL) combines multi-agent reinforcement learning (MARL) and federated learning (FL) so that multiple agents can exchange messages with a central server for co-operatively learning their local policies. However, a number of malicious …