Adaptive Latent-Space Constraints in Personalized FL

• URL: http://arxiv.org/abs/2505.07525v1
• Date: Mon, 12 May 2025 13:08:54 GMT
• Title: Adaptive Latent-Space Constraints in Personalized FL
• Authors: Sana Ayromlou, D. B. Emerson,
• Abstract summary: Federated learning (FL) has become an effective and widely used approach to training deep learning models on decentralized datasets held by distinct clients.<n>The efficacy of theoretically supported, adaptive MMD measures within the Ditto framework, a state-of-the-art technique in pFL, are investigated.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) has become an effective and widely used approach to training deep learning models on decentralized datasets held by distinct clients. FL also strengthens both security and privacy protections for training data. Common challenges associated with statistical heterogeneity between distributed datasets have spurred significant interest in personalized FL (pFL) methods, where models combine aspects of global learning with local modeling specific to each client's unique characteristics. In this work, the efficacy of theoretically supported, adaptive MMD measures within the Ditto framework, a state-of-the-art technique in pFL, are investigated. The use of such measures significantly improves model performance across a variety of tasks, especially those with pronounced feature heterogeneity. While the Ditto algorithm is specifically considered, such measures are directly applicable to a number of other pFL settings, and the results motivate the use of constraints tailored to the various kinds of heterogeneity expected in FL systems.

Related papers

• Not All Clients Are Equal: Personalized Federated Learning on Heterogeneous Multi-Modal Clients [52.14230635007546]
  Foundation models have shown remarkable capabilities across diverse multi-modal tasks, but their centralized training raises privacy concerns and induces high transmission costs.<n>For the growing demand for personalizing AI models for different user purposes, personalized federated learning (PFL) has emerged.<n>PFL allows each client to leverage the knowledge of other clients for further adaptation to individual user preferences, again without the need to share data.
  arXiv  Detail & Related papers  (2025-05-20T09:17:07Z)
• FedPref: Federated Learning Across Heterogeneous Multi-objective Preferences [2.519319150166215]
  Federated Learning (FL) is a distributed machine learning strategy developed for settings where training data is owned by distributed devices and cannot be shared.<n>The application of FL to real-world settings brings additional challenges associated with heterogeneity between participants.<n>We propose FedPref, a first algorithm designed to facilitate personalised FL in this setting.
  arXiv  Detail & Related papers  (2025-01-23T12:12:59Z)
• Client Contribution Normalization for Enhanced Federated Learning [4.726250115737579]
  Mobile devices, including smartphones and laptops, generate decentralized and heterogeneous data. Federated Learning (FL) offers a promising alternative by enabling collaborative training of a global model across decentralized devices without data sharing. This paper focuses on data-dependent heterogeneity in FL and proposes a novel approach leveraging mean latent representations extracted from locally trained models.
  arXiv  Detail & Related papers  (2024-11-10T04:03:09Z)
• FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning [9.084674176224109]
  Federated learning (FL) enables multiple clients with distributed data sources to collaboratively train a shared model without compromising data privacy. We introduce Federated Peer-Adaptive Ensemble Learning (FedPAE), a fully decentralized pFL algorithm that supports model heterogeneity and asynchronous learning. Our approach utilizes a peer-to-peer model sharing mechanism and ensemble selection to achieve a more refined balance between local and global information.
  arXiv  Detail & Related papers  (2024-10-17T22:47:19Z)
• On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness [16.595935469099306]
  We propose FLAME, an optimization framework by utilizing the alternating direction method of multipliers (ADMM) to train personalized and global models. Our theoretical analysis establishes the global convergence and two kinds of convergence rates for FLAME under mild assumptions. Our experimental findings show that FLAME outperforms state-of-the-art methods in convergence and accuracy, and it achieves higher test accuracy under various attacks.
  arXiv  Detail & Related papers  (2024-07-23T11:35:42Z)
• FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP Optimization [11.040916982022978]
  Federated Learning (FL) enables collaborative training of machine learning models on decentralized data. Data across clients often differs significantly due to class imbalance, feature distribution skew, sample size imbalance, and other phenomena. We propose a novel Bayesian PFL framework using bi-level optimization to tackle the data heterogeneity challenges.
  arXiv  Detail & Related papers  (2024-05-29T11:28:06Z)
• 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)
• Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning [49.72857433721424]
  Vision Transformers (ViT) and Visual Prompt Tuning (VPT) achieve state-of-the-art performance with improved efficiency in various computer vision tasks. We present a novel algorithm, SGPT, that integrates Generalized FL (GFL) and Personalized FL (PFL) approaches by employing a unique combination of both shared and group-specific prompts.
  arXiv  Detail & Related papers  (2023-10-27T17:22:09Z)
• 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)
• Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization [107.72786199113183]
  Federated learning (FL) provides a distributed learning framework for multiple participants to collaborate learning without sharing raw data. In this paper, we propose a novel Split-Mix FL strategy for heterogeneous participants that, once training is done, provides in-situ customization of model sizes and robustness.
  arXiv  Detail & Related papers  (2022-03-18T04:58:34Z)
• Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning [61.488646649045215]
  Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices)
  arXiv  Detail & Related papers  (2021-11-28T19:03:39Z)

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.