Forget to Generalize: Iterative Adaptation for Generalization in Federated Learning

• URL: http://arxiv.org/abs/2602.04536v1
• Date: Wed, 04 Feb 2026 13:27:15 GMT
• Title: Forget to Generalize: Iterative Adaptation for Generalization in Federated Learning
• Authors: Abdulrahman Alotaibi, Irene Tenison, Miriam Kim, Isaac Lee, Lalana Kagal,
• Abstract summary: We propose a new training paradigm - Iterative Federated Adaptation (IFA) - that enhances generalization in heterogeneous settings.<n>Experiments on CIFAR-10, MIT-Indoors, and Stanford Dogs datasets show that the proposed approach improves global accuracy.
• Score: 1.2559585990041289
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Web is naturally heterogeneous with user devices, geographic regions, browsing patterns, and contexts all leading to highly diverse, unique datasets. Federated Learning (FL) is an important paradigm for the Web because it enables privacy-preserving, collaborative machine learning across diverse user devices, web services and clients without needing to centralize sensitive data. However, its performance degrades severely under non-IID client distributions that is prevalent in real-world web systems. In this work, we propose a new training paradigm - Iterative Federated Adaptation (IFA) - that enhances generalization in heterogeneous federated settings through generation-wise forget and evolve strategy. Specifically, we divide training into multiple generations and, at the end of each, select a fraction of model parameters (a) randomly or (b) from the later layers of the model and reinitialize them. This iterative forget and evolve schedule allows the model to escape local minima and preserve globally relevant representations. Extensive experiments on CIFAR-10, MIT-Indoors, and Stanford Dogs datasets show that the proposed approach improves global accuracy, especially when the data cross clients are Non-IID. This method can be implemented on top any federated algorithm to improve its generalization performance. We observe an average of 21.5%improvement across datasets. This work advances the vision of scalable, privacy-preserving intelligence for real-world heterogeneous and distributed web systems.

Related papers

• Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data [19.33095080645165]
  Fed-REACT is a federated learning framework designed for heterogeneous and evolving client data.<n>We provide a theoretical analysis of the representation learning stage, and empirically demonstrate that Fed-REACT achieves superior accuracy and robustness on real-world datasets.
  arXiv  Detail & Related papers  (2025-09-08T20:24:40Z)
• Client-Centric Federated Adaptive Optimization [78.30827455292827]
  Federated Learning (FL) is a distributed learning paradigm where clients collaboratively train a model while keeping their own data private.<n>We propose Federated-Centric Adaptive Optimization, which is a class of novel federated optimization approaches.
  arXiv  Detail & Related papers  (2025-01-17T04:00:50Z)
• CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering [12.472038137777474]
  Human Activity Recognition (HAR) is vital for the automation and intelligent identification of human actions through data from diverse sensors. Traditional machine learning approaches by aggregating data on a central server and centralized processing are memory-intensive and raise privacy concerns. This work proposes CDFL, an efficient federated learning framework for image-based HAR.
  arXiv  Detail & Related papers  (2024-07-17T03:17:53Z)
• Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms [29.636944156801327]
  Multiple clients collaboratively train one global model without sharing their semantic parsing data. Lorar adjusts each client's contribution to the global model update based on its training loss reduction during each round. Clients with smaller datasets enjoy larger performance gains.
  arXiv  Detail & Related papers  (2023-05-26T19:25:49Z)
• Optimizing Server-side Aggregation For Robust Federated Learning via Subspace Training [80.03567604524268]
  Non-IID data distribution across clients and poisoning attacks are two main challenges in real-world federated learning systems. We propose SmartFL, a generic approach that optimize the server-side aggregation process. We provide theoretical analyses of the convergence and generalization capacity for SmartFL.
  arXiv  Detail & Related papers  (2022-11-10T13:20:56Z)
• Gradient Masked Averaging for Federated Learning [24.687254139644736]
  Federated learning allows a large number of clients with heterogeneous data to coordinate learning of a unified global model. Standard FL algorithms involve averaging of model parameters or gradient updates to approximate the global model at the server. We propose a gradient masked averaging approach for FL as an alternative to the standard averaging of client updates.
  arXiv  Detail & Related papers  (2022-01-28T08:42:43Z)
• Towards Fair Federated Learning with Zero-Shot Data Augmentation [123.37082242750866]
  Federated learning has emerged as an important distributed learning paradigm, where a server aggregates a global model from many client-trained models while having no access to the client data. We propose a novel federated learning system that employs zero-shot data augmentation on under-represented data to mitigate statistical heterogeneity and encourage more uniform accuracy performance across clients in federated networks. We study two variants of this scheme, Fed-ZDAC (federated learning with zero-shot data augmentation at the clients) and Fed-ZDAS (federated learning with zero-shot data augmentation at the server).
  arXiv  Detail & Related papers  (2021-04-27T18:23:54Z)
• Exploiting Shared Representations for Personalized Federated Learning [54.65133770989836]
  We propose a novel federated learning framework and algorithm for learning a shared data representation across clients and unique local heads for each client. Our algorithm harnesses the distributed computational power across clients to perform many local-updates with respect to the low-dimensional local parameters for every update of the representation. This result is of interest beyond federated learning to a broad class of problems in which we aim to learn a shared low-dimensional representation among data distributions.
  arXiv  Detail & Related papers  (2021-02-14T05:36:25Z)
• Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data [77.88594632644347]
  Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of heterogeneity across different clients' local datasets poses an optimization challenge. We propose a novel momentum-based method to mitigate this decentralized training difficulty.
  arXiv  Detail & Related papers  (2021-02-09T11:27:14Z)
• Analysis and Optimal Edge Assignment For Hierarchical Federated Learning on Non-IID Data [43.32085029569374]
  Federated learning algorithms aim to leverage distributed and diverse data stored at users' devices to learn a global phenomena. In the cases where the participants' data are strongly skewed (i.e., non-IID), the local models can overfit local data, leading to low performing global model. We propose a hierarchical learning system that performs Federated Gradient Descent on the user-edge layer and Federated Averaging on the edge-cloud layer.
  arXiv  Detail & Related papers  (2020-12-10T12:18:13Z)
• Multi-Center Federated Learning [62.57229809407692]
  This paper proposes a novel multi-center aggregation mechanism for federated learning. It learns multiple global models from the non-IID user data and simultaneously derives the optimal matching between users and centers. Our experimental results on benchmark datasets show that our method outperforms several popular federated learning methods.
  arXiv  Detail & Related papers  (2020-05-03T09:14:31Z)

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.