# Communication-Efficient Robust Federated Learning Over Heterogeneous Datasets

arXiv: https://arxiv.org/abs/2006.09992 · HTML: https://mlsanity.com/paper/2006.09992/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [BROADCAST: Reducing Both Stochastic and Compression Noise to Robustify Communication-Efficient Federated Learning](https://mlsanity.com/paper/2104.06685/) (2021)
- [Power Allocation for Wireless Federated Learning using Graph Neural Networks](https://mlsanity.com/paper/2111.07480/) (2021)
- [Learning to Transmit with Provable Guarantees in Wireless Federated Learning](https://mlsanity.com/paper/2304.09329/) (2023)
- [Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysis](https://mlsanity.com/paper/2303.17885/) (2023)

## Abstract (arXiv)

This work investigates fault-resilient federated learning when the data samples are non-uniformly distributed across workers, and the number of faulty workers is unknown to the central server. In the presence of adversarially faulty workers who may strategically corrupt datasets, the local messages exchanged (e.g., local gradients and/or local model parameters) can be unreliable, and thus the vanilla stochastic gradient descent (SGD) algorithm is not guaranteed to converge. Recently developed algorithms improve upon vanilla SGD by providing robustness to faulty workers at the price of slowing down convergence. To remedy this limitation, the present work introduces a fault-resilient proximal gradient (FRPG) algorithm that relies on Nesterov's acceleration technique. To reduce the communication overhead of FRPG, a local (L) FRPG algorithm is also developed to allow for intermittent server-workers parameter exchanges. For strongly convex loss functions, FRPG and LFRPG have provably faster convergence rates than a benchmark robust stochastic aggregation algorithm. Moreover, LFRPG converges faster than FRPG while using the same communication rounds. Numerical tests performed on various real datasets confirm the accelerated convergence of FRPG and LFRPG over the robust stochastic aggregation benchmark and competing alternatives.