FedERL: Federated Efficient and Robust Learning for Common Corruptions
This work addresses robustness and efficiency challenges in federated learning for edge devices, offering a practical solution for real-world applications, though it is incremental in combining existing concepts.
The paper tackles the problem of federated learning's vulnerability to common corruptions like noise and blur, while addressing client-side computational constraints, by proposing FedERL, which achieves robust training with zero overhead for clients and outperforms traditional methods in time and energy efficiency.
Federated learning (FL) accelerates the deployment of deep learning models on edge devices while preserving data privacy. However, FL systems face challenges due to client-side constraints on computational resources, and from a lack of robustness to common corruptions such as noise, blur, and weather effects. Existing robust training methods are computationally expensive and unsuitable for resource-constrained clients. We propose FedERL, federated efficient and robust learning, as the first work to explicitly address corruption robustness under time and energy constraints on the client side. At its core, FedERL employs a novel data-agnostic robust training (DART) method on the server to enhance robustness without access to the training data. In doing so, FedERL ensures zero robustness overhead for clients. Extensive experiments demonstrate FedERL's ability to handle common corruptions at a fraction of the time and energy cost of traditional robust training methods. In scenarios with limited time and energy budgets, FedERL surpasses the performance of traditional robust training, establishing it as a practical and scalable solution for real-world FL applications.