# Federated Learning in the Presence of Adversarial Client Unavailability

arXiv: https://arxiv.org/abs/2305.19971 · HTML: https://mlsanity.com/paper/2305.19971/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 43 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 28 of 28)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [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)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming](https://mlsanity.com/paper/1309.5549/) (2013)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [Federated Learning with Matched Averaging](https://mlsanity.com/paper/2002.06440/) (2020)
- [Robust Aggregation for Federated Learning](https://mlsanity.com/paper/1912.13445/) (2019)
- [Lower Bounds for Non-Convex Stochastic Optimization](https://mlsanity.com/paper/1912.02365/) (2019)
- [Learning from History for Byzantine Robust Optimization](https://mlsanity.com/paper/2012.10333/) (2020)
- [Communication Efficient Distributed Optimization using an Approximate Newton-type Method](https://mlsanity.com/paper/1312.7853/) (2013)
- [Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing](https://mlsanity.com/paper/2006.09365/) (2020)
- [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)
- [Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity](https://mlsanity.com/paper/2302.01772/) (2023)
- [FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning](https://mlsanity.com/paper/2207.14130/) (2022)
- [Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums](https://mlsanity.com/paper/2205.12173/) (2022)
- [Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees](https://mlsanity.com/paper/2006.14591/) (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)
- [On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond](https://mlsanity.com/paper/2206.05187/) (2022)
- [Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability](https://mlsanity.com/paper/2002.07399/) (2020)
- [Distributed Robust Learning](https://mlsanity.com/paper/1409.5937/) (2014)
- [Approximate Agreement under Mobile Byzantine Faults](https://mlsanity.com/paper/1604.03871/) (2016)

## Built on by (most cited first, 5 of 5)

- [Towards Bias Correction of FedAvg over Nonuniform and Time-Varying Communications](https://mlsanity.com/paper/2306.00280/) (2023)
- [Empowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics](https://mlsanity.com/paper/2404.10091/) (2024)
- [FedOAED: Federated On-Device Autoencoder Denoiser for Heterogeneous Data under Limited Client Availability](https://mlsanity.com/paper/2512.17986/) (2025)
- [FedAdaVR: Adaptive Variance Reduction for Robust Federated Learning under Limited Client Participation](https://mlsanity.com/paper/2601.22204/) (2026)
- [Over-the-Air Federated Learning in Heterogeneous Mobile Wireless Networks](https://mlsanity.com/paper/2609.32832/) (2026)

## Abstract (arXiv)

Federated learning is a decentralized machine learning framework that enables collaborative model training without revealing raw data. Due to the diverse hardware and software limitations, a client may not always be available for the computation requests from the parameter server. An emerging line of research is devoted to tackling arbitrary client unavailability. However, existing work still imposes structural assumptions on the unavailability patterns, impeding their applicability in challenging scenarios wherein the unavailability patterns are beyond the control of the parameter server. Moreover, in harsh environments like battlefields, adversaries can selectively and adaptively silence specific clients. In this paper, we relax the structural assumptions and consider adversarial client unavailability. To quantify the degrees of client unavailability, we use the notion of ε-adversary dropout fraction. We show that simple variants of FedAvg or FedProx, albeit completely agnostic to ε, converge to an estimation error on the order of ε (G2 + σ2) for non-convex global objectives and ε(G2 + σ2)/μ2 for μ strongly convex global objectives, where G is a heterogeneity parameter and σ2 is the noise level. Conversely, we prove that any algorithm has to suffer an estimation error of at least ε (G2 + σ2)/8 and ε(G2 + σ2)/(8μ2) for non-convex global objectives and μ-strongly convex global objectives. Furthermore, the convergence speeds of the FedAvg or FedProx variants are O(1/√T) for non-convex objectives and O(1/T) for strongly-convex objectives, both of which are the best possible for any first-order method that only has access to noisy gradients.