# Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis

arXiv: https://arxiv.org/abs/2410.23131 · HTML: https://mlsanity.com/paper/2410.23131/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 53 references that are in the map.
- First posted on arXiv 30 Oct 2024.

## Builds on (most cited first, 30 of 34)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (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)
- [Local SGD Converges Fast and Communicates Little](https://mlsanity.com/paper/1805.09767/) (2018)
- [A Field Guide to Federated Optimization](https://mlsanity.com/paper/2107.06917/) (2021)
- [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)
- [Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning](https://mlsanity.com/paper/2101.11203/) (2021)
- [Asynchronous Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1710.06952/) (2017)
- [Collecting Telemetry Data Privately](https://mlsanity.com/paper/1712.01524/) (2017)
- [Optimal Client Sampling for Federated Learning](https://mlsanity.com/paper/2010.13723/) (2020)
- [Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms](https://mlsanity.com/paper/1808.07576/) (2018)
- [Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning](https://mlsanity.com/paper/2105.05883/) (2021)
- [Papaya: Practical, Private, and Scalable Federated Learning](https://mlsanity.com/paper/2111.04877/) (2021)
- [Federated Accelerated Stochastic Gradient Descent](https://mlsanity.com/paper/2006.08950/) (2020)
- [Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications](https://mlsanity.com/paper/2102.08503/) (2021)
- [Fast Federated Learning in the Presence of Arbitrary Device Unavailability](https://mlsanity.com/paper/2106.04159/) (2021)
- [Towards Flexible Device Participation in Federated Learning](https://mlsanity.com/paper/2006.06954/) (2020)
- [A Unified Analysis of Federated Learning with Arbitrary Client Participation](https://mlsanity.com/paper/2205.13648/) (2022)
- [Anarchic Federated Learning](https://mlsanity.com/paper/2108.09875/) (2021)
- [Semi-Cyclic Stochastic Gradient Descent](https://mlsanity.com/paper/1904.10120/) (2019)
- [Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective](https://mlsanity.com/paper/2111.03741/) (2021)
- [Momentum Benefits Non-IID Federated Learning Simply and Provably](https://mlsanity.com/paper/2306.16504/) (2023)
- [Learning Differentially Private Recurrent Language Models](https://mlsanity.com/paper/1710.06963/) (2017)
- [On the Convergence of Federated Averaging with Cyclic Client Participation](https://mlsanity.com/paper/2302.03109/) (2023)
- [Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability](https://mlsanity.com/paper/2002.07399/) (2020)

## Built on by (most cited first, 8 of 8)

- [GFPL: Generative Federated Prototype Learning for Resource-Constrained and Data-Imbalanced Vision Task](https://mlsanity.com/paper/2602.21873/) (2026)
- [Computation-aware Energy-harvesting Federated Learning with Pipelined Cyclic Scheduling](https://mlsanity.com/paper/2511.11949/) (2025)
- [Communication-Efficient Federated AUC Maximization with Cyclic Client Participation](https://mlsanity.com/paper/2601.01649/) (2026)
- [Ordered Local Momentum for Asynchronous Distributed Learning under Arbitrary Delays](https://mlsanity.com/paper/2601.12322/) (2026)
- [RefProtoFL: Communication-Efficient Federated Learning via External-Referenced Prototype Alignment](https://mlsanity.com/paper/2601.14746/) (2026)
- [Federated Learning of Binary Neural Networks: Enabling Low-Cost Inference](https://mlsanity.com/paper/2603.15507/) (2026)
- [Agentic Federated Learning: The Future of Distributed Training Orchestration](https://mlsanity.com/paper/2604.04895/) (2026)
- [Over-the-Air Federated Learning in Heterogeneous Mobile Wireless Networks](https://mlsanity.com/paper/2609.32832/) (2026)

## Abstract (arXiv)

In federated learning, it is common to assume that clients are always available to participate in training, which may not be feasible with user devices in practice. Recent works analyze federated learning under more realistic participation patterns, such as cyclic client availability or arbitrary participation. However, all such works either require strong assumptions (e.g., all clients participate almost surely within a bounded window), do not achieve linear speedup and reduced communication rounds, or are not applicable in the general non-convex setting. In this work, we focus on nonconvex optimization and consider participation patterns in which the chance of participation over a fixed window of rounds is equal among all clients, which includes cyclic client availability as a special case. Under this setting, we propose a new algorithm, named Amplified SCAFFOLD, and prove that it achieves linear speedup, reduced communication, and resilience to data heterogeneity simultaneously. In particular, for cyclic participation, our algorithm is proved to enjoy O(ε-2) communication rounds to find an ε-stationary point in the non-convex stochastic setting. In contrast, the prior work under the same setting requires O(kappa2 ε-4) communication rounds, where kappa denotes the data heterogeneity. Therefore, our algorithm significantly reduces communication rounds due to better dependency in terms of ε and kappa. Our analysis relies on a fine-grained treatment of the nested dependence between client participation and errors in the control variates, which results in tighter guarantees than previous work. We also provide experimental results with (1) synthetic data and (2) real-world data with a large number of clients (N = 250), demonstrating the effectiveness of our algorithm under periodic client participation.