Client and Training Data Selection for Computationally Efficient Synchronized Federated Learning

Muzaffer Citir; Hiroki Nishikawa; Sangyoung Park

cs.LGSep 30, 2026

Client and Training Data Selection for Computationally Efficient Synchronized Federated Learning

Authors: Muzaffer Citir, Hiroki Nishikawa, Sangyoung Park

Organizations: Smart Mobility Systems, Technical University of Berlin, Germany · Graduate School of Information Science and Technology, The University of Osaka, Japan

Abstract

Federated learning (FL) is a promising paradigm of machine learning, which preserves user privacy by enabling learning without sharing raw data with a cloud server. Straggling clients have been a problem for FL as they introduce delays in aggregating the local models and hence, the convergence of the global model. Therefore, it is important to have a mechanism that ensures fast convergence of the global model as well as good FL participation rate. Another issue for the convergence of a model in FL is the non-independent and identically distributed (non-iid) data across the clients. Prior approaches based on probabilistic client selection do not work well under non-iid data especially when the number of clients is small. We show scenarios where such approaches fail and propose a joint client-training data selection algorithm for fast convergence of FL models. Our experiments on CIFAR-100 dataset show that convergence of the FL model can be significantly improved over prior works that can consider non-iid data and heterogeneous computation and higher model accuracy.

Figures & tables

Fig. 1: (a) A deadline miss example of FL devices scheduling, and (b) avoiding it with proper data allocation.

Fig. 2: Training time vs dataset size for CIFAR-10 dataset.

Fig. 3: Time distribution with 5

\times

10^{3}

images of CIFAR-10.

Component	Specification
CPU	Intel i9-12900KF 3,2 GHz
GPU	NVIDIA GeForce RTX 4090 24 GB (CUDA 12.2)
RAM	64 GB DDR5-SDRAM 4800 MHz
Operating System	Ubuntu 22.04.3 LTS

TABLE I: Hardware specifications for FL Training

Fig. 4: Test accuracy over time for the proposed, probPart and MinCost algorithms for CIFAR-100 dataset distributed across 50 clients. Shaded area denotes 1 std range over 6 experiments with random seeds for data distribution.

Fig. 5: Component ablation of the usefulness metric (Eq. 13 ) as test accuracy over training time, under two degrees of non-iid severity: (a)

\alpha=0.3

and (b)

\alpha=0.1

. Each variant disables one term of

u_{k}

; for

w_{1}

, its two sub-factors (the freshness penalty and the client dataset-size preference) are disabled separately. Proposed keeps all terms, while probPart and MinCost are shown for reference. The experiments use the same client, model, and deadline configuration as Fig. 4 , and are reported for a single random seed.

Method	Overall	Tail-10	CV	Gini
	acc. (%)	acc. (%)
Proposed (full)	42.3	15.3	0.43	0.24
probPart	38.0	6.8	0.49	0.28
MinCost	31.2	3.5	0.62	0.35

TABLE II: Per-class fairness on the CIFAR-100 test set at

\alpha=0.3

for each method’s final trained model (single run): overall accuracy, tail-class accuracy (mean of the 10 lowest-accuracy classes), and the dispersion of the per-class accuracies (coefficient of variation and Gini coefficient; lower is more uniform).

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Aug 3, 2026, cs.LGJ/K move · Enter open · S save

Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning

Yuan-Heng Tsai, Li-Hsing Yen, Yan-Wei Chen

Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.

Federated Learning Distributions Impact Analysis

Open paper AskPDF arXiv

Federated learning (FL) is a popular distributed learning framework where multiple clients perform local training and a server aggregates the locally updated models. FL enables decentralized training while preserving the privacy of clients' datasets. However, non-independent and identically distributed (non-IID) or noisy datasets can lead to low model accuracy or high convergence latency. Precluding these clients through client selection may mitigate the problem, but heavily biased client selections may also degrade the learning performance. In this study, we first experimentally measure the impact of non-IID data (including skews in data quantity and label distribution), noisy data, and fairness in client selection on model accuracy and convergence. We then propose a privacy-preserving scoring method to assess each client's contribution in FL, with experiments conducted to demonstrate the effectiveness of the proposed assessment.

Abstract

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