# Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints

arXiv: https://arxiv.org/abs/2205.06730 · HTML: https://mlsanity.com/paper/2205.06730/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 55 references that are in the map.
- First posted on arXiv 13 May 2022.

## Builds on (most cited first, 30 of 35)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [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)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Local SGD Converges Fast and Communicates Little](https://mlsanity.com/paper/1805.09767/) (2018)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Federated Learning Based on Dynamic Regularization](https://mlsanity.com/paper/2111.04263/) (2021)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Sparsified SGD with Memory](https://mlsanity.com/paper/1809.07599/) (2018)
- [A Field Guide to Federated Optimization](https://mlsanity.com/paper/2107.06917/) (2021)
- [The Non-IID Data Quagmire of Decentralized Machine Learning](https://mlsanity.com/paper/1910.00189/) (2019)
- [Applied Federated Learning: Improving Google Keyboard Query Suggestions](https://mlsanity.com/paper/1812.02903/) (2018)
- [Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies](https://mlsanity.com/paper/2010.01243/) (2020)
- [Federated Learning with Buffered Asynchronous Aggregation](https://mlsanity.com/paper/2106.06639/) (2021)
- [Distributed Mean Estimation with Limited Communication](https://mlsanity.com/paper/1611.00429/) (2016)
- [Communication Efficient Distributed Optimization using an Approximate Newton-type Method](https://mlsanity.com/paper/1312.7853/) (2013)
- [DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression](https://mlsanity.com/paper/1905.05957/) (2019)
- [LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning](https://mlsanity.com/paper/1805.09965/) (2018)
- [Stochastic Optimization with Importance Sampling](https://mlsanity.com/paper/1401.2753/) (2014)
- [Natural Compression for Distributed Deep Learning](https://mlsanity.com/paper/1905.10988/) (2019)
- [On Large-Cohort Training for Federated Learning](https://mlsanity.com/paper/2106.07820/) (2021)
- [Multi-Armed Bandit Based Client Scheduling for Federated Learning](https://mlsanity.com/paper/2007.02315/) (2020)
- [Semi-Cyclic Stochastic Gradient Descent](https://mlsanity.com/paper/1904.10120/) (2019)

## Built on by (most cited first, 30 of 30)

- [Client Selection in Federated Learning: Principles, Challenges, and Opportunities](https://mlsanity.com/paper/2211.01549/) (2022)
- [Federated Learning under Heterogeneous and Correlated Client Availability](https://mlsanity.com/paper/2301.04632/) (2023)
- [Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations](https://mlsanity.com/paper/2405.09839/) (2024)
- [Green Federated Learning: A new era of Green Aware AI](https://mlsanity.com/paper/2409.12626/) (2024)
- [FedGS: Federated Graph-based Sampling with Arbitrary Client Availability](https://mlsanity.com/paper/2211.13975/) (2022)
- [MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates](https://mlsanity.com/paper/2306.12212/) (2023)
- [Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability](https://mlsanity.com/paper/2409.17446/) (2024)
- [Communication-Efficient Federated Learning for LEO Satellite Networks Integrated with HAPs Using Hybrid NOMA-OFDM](https://mlsanity.com/paper/2401.00685/) (2024)
- [FedStale: leveraging stale client updates in federated learning](https://mlsanity.com/paper/2405.04171/) (2024)
- [Federated Learning Under Restricted User Availability](https://mlsanity.com/paper/2309.14176/) (2023)
- [Riemannian Federated Learning via Averaging Gradient Streams](https://mlsanity.com/paper/2409.07223/) (2024)
- [Collaborative Policy Learning for Dynamic Scheduling Tasks in Cloud-Edge-Terminal IoT Networks Using Federated Reinforcement Learning](https://mlsanity.com/paper/2307.00541/) (2023)
- [Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics](https://mlsanity.com/paper/2404.10091/) (2024)
- [Analyzing the Impact of Participant Failures in Cross-Silo Federated Learning](https://mlsanity.com/paper/2511.14456/) (2025)
- [On-Demand Model and Client Deployment in Federated Learning with Deep Reinforcement Learning](https://mlsanity.com/paper/2405.07175/) (2024)
- [Debiasing Federated Learning with Correlated Client Participation](https://mlsanity.com/paper/2410.01209/) (2024)
- [Communication-Efficient Federated Learning under Dynamic Device Arrival and Departure: Convergence Analysis and Algorithm Design](https://mlsanity.com/paper/2410.05662/) (2024)
- [FedAVOT: Exact Distribution Alignment in Federated Learning via Masked Optimal Transport](https://mlsanity.com/paper/2509.14444/) (2025)
- [Towards Fairness in Provably Communication-Efficient Federated Recommender Systems](https://mlsanity.com/paper/2405.15788/) (2024)
- [RIFLES: Resource-effIcient Federated LEarning via Scheduling](https://mlsanity.com/paper/2505.13169/) (2025)
- [Communication-Efficient Federated Learning with Adaptive Number of Participants](https://mlsanity.com/paper/2508.13803/) (2025)
- [Semi-decentralized Federated Time Series Prediction with Client Availability Budgets](https://mlsanity.com/paper/2509.03660/) (2025)
- [Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Scheduling](https://mlsanity.com/paper/2511.11949/) (2025)
- [RefProtoFL: Communication-Efficient Federated Learning via External-Referenced Prototype Alignment](https://mlsanity.com/paper/2601.14746/) (2026)
- [Revealing the influence of participant failures on model quality in cross-silo Federated Learning](https://mlsanity.com/paper/2603.25289/) (2026)
- [Robust Synchronisation for Federated Learning in The Face of Correlated Device Failure](https://mlsanity.com/paper/2604.16090/) (2026)
- [FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training](https://mlsanity.com/paper/2605.02125/) (2026)
- [Federated Client Selection under Partial Visibility: A POMDP Approach with Spatio-Temporal Attention](https://mlsanity.com/paper/2605.11752/) (2026)
- [FedeRage: Provably Convergent Agnostic Federated Learning under General Client Drift](https://mlsanity.com/paper/2609.21057/) (2026)
- [Over-the-Air Federated Learning in Heterogeneous Mobile Wireless Networks](https://mlsanity.com/paper/2609.32832/) (2026)

## Abstract (arXiv)

Federated learning systems facilitate training of global models in settings where potentially heterogeneous data is distributed across a large number of clients. Such systems operate in settings with intermittent client availability and/or time-varying communication constraints. As a result, the global models trained by federated learning systems may be biased towards clients with higher availability. We propose F3AST, an unbiased algorithm that dynamically learns an availability-dependent client selection strategy which asymptotically minimizes the impact of client-sampling variance on the global model convergence, enhancing performance of federated learning. The proposed algorithm is tested in a variety of settings for intermittently available clients under communication constraints, and its efficacy demonstrated on synthetic data and realistically federated benchmarking experiments using CIFAR100 and Shakespeare datasets. We show up to 186% and 8% accuracy improvements over FedAvg, and 8% and 7% over FedAdam on CIFAR100 and Shakespeare, respectively.