FedSUM Family: Efficient Federated Learning Methods under Arbitrary Client Participation
Runze You, Shi Pu
TL;DR
The paper introduces the FedSUM family of federated learning algorithms that efficiently handle arbitrary client participation patterns, supported by theoretical convergence guarantees, thus enhancing real-world applicability.
Contribution
It proposes a unified framework with three variants for federated learning under arbitrary participation, addressing data heterogeneity without extra assumptions.
Findings
Supports arbitrary client participation patterns.
Provides convergence guarantees for diverse scenarios.
Includes communication-efficient variant.
Abstract
Federated Learning (FL) methods are often designed for specific client participation patterns, limiting their applicability in practical deployments. We introduce the FedSUM family of algorithms, which supports arbitrary client participation without additional assumptions on data heterogeneity. Our framework models participation variability with two delay metrics, the maximum delay and the average delay . The FedSUM family comprises three variants: FedSUM-B (basic version), FedSUM (standard version), and FedSUM-CR (communication-reduced version). We provide unified convergence guarantees demonstrating the effectiveness of our approach across diverse participation patterns, thereby broadening the applicability of FL in real-world scenarios.
Peer Reviews
Decision·Submitted to ICLR 2026
1. By unifying several participation schemes into a single theoretical lens, the FedSUM family provides a flexible and general approach. 2. It (partially) addresses the problem of unpredictable participation from a theoretical perspective, bridging the gap between practical and theoretical studies of FL.
1. Evaluations are limited to standard vision datasets (MNIST, SVHN, CIFAR-10). No experiments on larger or non-IID real-world datasets (e.g., cross-device or cross-silo FL). Adding more realistic experiments would significantly improve the soundness of the paper. 2. The unified framework seems fail to capture time-dependent client participation. For example, shown in (Ribero et al., 2022, Sun et al, 2025), the participation pattern is Markovian, which do not fit in the proposed framework. More
1. The use of maximum delay ($\tau_{max}$) and average delay ($\tau_{avg}$) provides an elegant and intuitive way to quantify participation variability and enables a unified convergence analysis for the FedSUM algorithms. 2. The FedSUM family subsumes many client participation scenarios (random, cyclic, reshuffled, etc.) under a single analytical umbrella, offering broad applicability. 3. The convergence rates recover the known rates in special cases and are derived under standard assumptions (s
1. While the delay metrics are central to the theoretical framework, empirical results do not explicitly demonstrate how varying $\tau_{max}$ or $\tau_{avg}$ impacts performance or convergence speed. An ablation study varying delay levels across synthetic participation schemes would be beneficial. 2. While FedSUM-CR emphasizes communication savings and acknowledges additional memory overhead, neither the communication cost nor the memory overhead is quantitatively analyzed. 3. The evaluation is
1. The arbitrary participation setting, characterized maximum delay ($\tau_{max}$) and average delay ($\tau_{avg}$), captures a wide range of realistic client behaviors, which significantly broadens applicability beyond common uniform sampling frameworks. 2. The convergence guarantees are presented in a unified manner for all three FedSUM variants, recovering known results in canonical special cases.
1. Datasets are small to medium scale (MNIST, SVHN, CIFAR-10). 2. Client participation patterns in experiment does not perfectly align with the four participation examples.
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Opportunistic and Delay-Tolerant Networks · Mobile Crowdsensing and Crowdsourcing