# FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification

arXiv: https://arxiv.org/abs/2407.19103 · HTML: https://mlsanity.com/paper/2407.19103/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 33 references that are in the map.
- First posted on arXiv 26 Jul 2024.

## Builds on (most cited first, 19 of 19)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [An Efficient Framework for Clustered Federated Learning](https://mlsanity.com/paper/2006.04088/) (2020)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [Federated learning with hierarchical clustering of local updates to improve training on non-IID data](https://mlsanity.com/paper/2004.11791/) (2020)
- [FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout](https://mlsanity.com/paper/2102.13451/) (2021)
- [Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2111.14213/) (2021)
- [Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning](https://mlsanity.com/paper/2105.05883/) (2021)
- [Measure Contribution of Participants in Federated Learning](https://mlsanity.com/paper/1909.08525/) (2019)
- [Fast Federated Learning in the Presence of Arbitrary Device Unavailability](https://mlsanity.com/paper/2106.04159/) (2021)
- [Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling](https://mlsanity.com/paper/2112.11256/) (2021)
- [FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning](https://mlsanity.com/paper/2207.14130/) (2022)
- [A Unified Analysis of Federated Learning with Arbitrary Client Participation](https://mlsanity.com/paper/2205.13648/) (2022)
- [Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning](https://mlsanity.com/paper/2012.08009/) (2020)
- [FedGS: Federated Graph-based Sampling with Arbitrary Client Availability](https://mlsanity.com/paper/2211.13975/) (2022)
- [A Survey of Federated Evaluation in Federated Learning](https://mlsanity.com/paper/2305.08070/) (2023)

## Abstract (arXiv)

Federated learning (FL) enables clients to collaboratively train machine learning models under the coordination of a server in a privacy-preserving manner. One of the main challenges in FL is that the server may not receive local updates from each client in each round due to client resource limitations and intermittent network connectivity. The existence of unavailable clients severely deteriorates the overall FL performance. In this paper, we propose , a novel client update Approximation and Rectification algorithm for FL to address the client unavailability issue. FedAR can get all clients involved in the global model update to achieve a high-quality global model on the server, which also furnishes accurate predictions for each client. To this end, the server uses the latest update from each client as a surrogate for its current update. It then assigns a different weight to each client's surrogate update to derive the global model, in order to guarantee contributions from both available and unavailable clients. Our theoretical analysis proves that FedAR achieves optimal convergence rates on non-IID datasets for both convex and non-convex smooth loss functions. Extensive empirical studies show that FedAR comprehensively outperforms state-of-the-art FL baselines including FedAvg, MIFA, FedVARP and Scaffold in terms of the training loss, test accuracy, and bias mitigation. Moreover, FedAR also depicts impressive performance in the presence of a large number of clients with severe client unavailability.