Fractional-Order Federated Learning

• URL: http://arxiv.org/abs/2602.15380v1
• Date: Tue, 17 Feb 2026 06:25:23 GMT
• Title: Fractional-Order Federated Learning
• Authors: Mohammad Partohaghighi, Roummel Marcia, YangQuan Chen,
• Abstract summary: Federated learning (FL) allows remote clients to train a global model collaboratively while protecting client privacy.<n>Despite its privacy-preserving benefits, FL has significant drawbacks, including slow convergence, high communication cost, and non-independent-and-identically-distributed (non-IID) data.
• Score: 4.1751058176413105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) allows remote clients to train a global model collaboratively while protecting client privacy. Despite its privacy-preserving benefits, FL has significant drawbacks, including slow convergence, high communication cost, and non-independent-and-identically-distributed (non-IID) data. In this work, we present a novel FedAvg variation called Fractional-Order Federated Averaging (FOFedAvg), which incorporates Fractional-Order Stochastic Gradient Descent (FOSGD) to capture long-range relationships and deeper historical information. By introducing memory-aware fractional-order updates, FOFedAvg improves communication efficiency and accelerates convergence while mitigating instability caused by heterogeneous, non-IID client data. We compare FOFedAvg against a broad set of established federated optimization algorithms on benchmark datasets including MNIST, FEMNIST, CIFAR-10, CIFAR-100, EMNIST, the Cleveland heart disease dataset, Sent140, PneumoniaMNIST, and Edge-IIoTset. Across a range of non-IID partitioning schemes, FOFedAvg is competitive with, and often outperforms, these baselines in terms of test performance and convergence speed. On the theoretical side, we prove that FOFedAvg converges to a stationary point under standard smoothness and bounded-variance assumptions for fractional order $0<α\le 1$. Together, these results show that fractional-order, memory-aware updates can substantially improve the robustness and effectiveness of federated learning, offering a practical path toward distributed training on heterogeneous data.

Related papers

• Roughness-Informed Federated Learning [3.8218584696400484]
  Federated Learning (FL) enables collaborative model training across distributed clients.<n>FL faces challenges in non-independent and identically distributed (non-IID) settings due to client drift.<n>We propose RI-FedAvg, a novel FL that mitigates client drift by incorporating a Roughness Index (RI)-based regularization term.
  arXiv  Detail & Related papers  (2026-02-11T07:35:45Z)
• CO-PFL: Contribution-Oriented Personalized Federated Learning for Heterogeneous Networks [51.43780477302533]
  Contribution-Oriented PFL (CO-PFL) is a novel algorithm that dynamically estimates each client's contribution for global aggregation.<n>CO-PFL consistently surpasses state-of-the-art methods in robustness in personalization accuracy, robustness, scalability and convergence stability.
  arXiv  Detail & Related papers  (2025-10-23T05:10:06Z)
• Aiding Global Convergence in Federated Learning via Local Perturbation and Mutual Similarity Information [6.767885381740953]
  Federated learning has emerged as a distributed optimization paradigm. We propose a novel modified framework wherein each client locally performs a perturbed gradient step. We show that our algorithm speeds convergence up to a margin of 30 global rounds compared with FedAvg.
  arXiv  Detail & Related papers  (2024-10-07T23:14:05Z)
• An Aggregation-Free Federated Learning for Tackling Data Heterogeneity [50.44021981013037]
  Federated Learning (FL) relies on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework, where clients update local models based on a global model aggregated by the server from the previous training round. We introduce FedAF, a novel aggregation-free FL algorithm.
  arXiv  Detail & Related papers  (2024-04-29T05:55:23Z)
• Personalized Federated Learning under Mixture of Distributions [98.25444470990107]
  We propose a novel approach to Personalized Federated Learning (PFL), which utilizes Gaussian mixture models (GMM) to fit the input data distributions across diverse clients. FedGMM possesses an additional advantage of adapting to new clients with minimal overhead, and it also enables uncertainty quantification. Empirical evaluations on synthetic and benchmark datasets demonstrate the superior performance of our method in both PFL classification and novel sample detection.
  arXiv  Detail & Related papers  (2023-05-01T20:04:46Z)
• Adaptive Federated Learning via New Entropy Approach [14.595709494370372]
  Federated Learning (FL) has emerged as a prominent distributed machine learning framework. In this paper, we propose an adaptive FEDerated learning algorithm based on ENTropy theory (FedEnt) to alleviate the parameter deviation among heterogeneous clients.
  arXiv  Detail & Related papers  (2023-03-27T07:57:04Z)
• FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregation [95.85026305874824]
  We introduce a data-driven approach called FedSkip to improve the client optima by periodically skipping federated averaging and scattering local models to the cross devices. We conduct extensive experiments on a range of datasets to demonstrate that FedSkip achieves much higher accuracy, better aggregation efficiency and competing communication efficiency.
  arXiv  Detail & Related papers  (2022-12-14T13:57:01Z)
• FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learning [91.74206675452888]
  We propose a novel method FedFM, which guides each client's features to match shared category-wise anchors. To achieve higher efficiency and flexibility, we propose a FedFM variant, called FedFM-Lite, where clients communicate with server with fewer synchronization times and communication bandwidth costs.
  arXiv  Detail & Related papers  (2022-10-14T08:11:34Z)
• Stochastic Coded Federated Learning with Convergence and Privacy Guarantees [8.2189389638822]
  Federated learning (FL) has attracted much attention as a privacy-preserving distributed machine learning framework. This paper proposes a coded federated learning framework, namely coded federated learning (SCFL) to mitigate the straggler issue. We characterize the privacy guarantee by the mutual information differential privacy (MI-DP) and analyze the convergence performance in federated learning.
  arXiv  Detail & Related papers  (2022-01-25T04:43:29Z)
• FedPrune: Towards Inclusive Federated Learning [1.308951527147782]
  Federated learning (FL) is a distributed learning technique that trains a shared model over distributed data in a privacy-preserving manner. We propose FedPrune; a system that tackles this challenge by pruning the global model for slow clients based on their device characteristics. By using insights from Central Limit Theorem, FedPrune incorporates a new aggregation technique that achieves robust performance over non-IID data.
  arXiv  Detail & Related papers  (2021-10-27T06:33:38Z)
• A Unified Linear Speedup Analysis of Federated Averaging and Nesterov FedAvg [49.76940694847521]
  Federated learning (FL) learns a model jointly from a set of participating devices without sharing each other's privately held data. In this paper, we focus on Federated Averaging (FedAvg), one of the most popular and effective FL algorithms in use today. We show that FedAvg enjoys linear speedup in each case, although with different convergence rates and communication efficiencies.
  arXiv  Detail & Related papers  (2020-07-11T05:59:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.