Adaptive Expert Models for Personalization in Federated Learning

• URL: http://arxiv.org/abs/2206.07832v1
• Date: Wed, 15 Jun 2022 22:05:36 GMT
• Title: Adaptive Expert Models for Personalization in Federated Learning
• Authors: Martin Isaksson, Edvin Listo Zec, Rickard C\"oster, Daniel Gillblad, \v{S}ar\=unas Girdzijauskas
• Abstract summary: Federated Learning (FL) is a promising framework for distributed learning when data is private and sensitive. We propose a practical and robust approach to personalization in FL that adjusts to heterogeneous and non-IID data. We show that our approach achieves an accuracy up to 29.78 % and up to 4.38 % better compared to a local model in a pathological non-IID setting.
• Score: 0.9449650062296824
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) is a promising framework for distributed learning when data is private and sensitive. However, the state-of-the-art solutions in this framework are not optimal when data is heterogeneous and non-Independent and Identically Distributed (non-IID). We propose a practical and robust approach to personalization in FL that adjusts to heterogeneous and non-IID data by balancing exploration and exploitation of several global models. To achieve our aim of personalization, we use a Mixture of Experts (MoE) that learns to group clients that are similar to each other, while using the global models more efficiently. We show that our approach achieves an accuracy up to 29.78 % and up to 4.38 % better compared to a local model in a pathological non-IID setting, even though we tune our approach in the IID setting.

Related papers

• 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. We propose Federated-Centric Adaptive Optimization, which is a class of novel federated optimization approaches.
  arXiv  Detail & Related papers  (2025-01-17T04:00:50Z)
• Personalized Federated Learning with Adaptive Feature Aggregation and Knowledge Transfer [0.0]
  Federated Learning (FL) is popular as a privacy-preserving machine learning paradigm for generating a single model on decentralized data. We propose a new method personalized Federated learning with Adaptive Feature Aggregation and Knowledge Transfer (FedAFK) We conduct extensive experiments on three datasets in two widely-used heterogeneous settings and show the superior performance of our proposed method over thirteen state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-10-19T11:32:39Z)
• 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)
• Federated Learning with Projected Trajectory Regularization [65.6266768678291]
  Federated learning enables joint training of machine learning models from distributed clients without sharing their local data. One key challenge in federated learning is to handle non-identically distributed data across the clients. We propose a novel federated learning framework with projected trajectory regularization (FedPTR) for tackling the data issue.
  arXiv  Detail & Related papers  (2023-12-22T02:12:08Z)
• PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning [16.344719695572586]
  We propose a novel scheme to inject personalized prior knowledge into a global model in each client. At the heart of our proposed approach is a framework, the PFL with Bregman Divergence (pFedBreD) Our method reaches the state-of-the-art performances on 5 datasets and outperforms other methods by up to 3.5% across 8 benchmarks.
  arXiv  Detail & Related papers  (2023-10-13T15:21:25Z)
• FedJETs: Efficient Just-In-Time Personalization with Federated Mixture of Experts [48.78037006856208]
  FedJETs is a novel solution by using a Mixture-of-Experts (MoE) framework within a Federated Learning (FL) setup. Our method leverages the diversity of the clients to train specialized experts on different subsets of classes, and a gating function to route the input to the most relevant expert(s) Our approach can improve accuracy up to 18% in state of the art FL settings, while maintaining competitive zero-shot performance.
  arXiv  Detail & Related papers  (2023-06-14T15:47:52Z)
• FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning [4.02923738318937]
  Uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. This work introduces a novel non-IID type encountered in real-world datasets, namely cluster-skew. We propose FedDRL, a novel FL model that employs deep reinforcement learning to adaptively determine each client's impact factor.
  arXiv  Detail & Related papers  (2022-08-04T04:24:16Z)
• Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization [57.98426940386627]
  We show that coordinating local learning with private centralized learning yields a generically useful and improved tradeoff between accuracy and privacy. We illustrate our theoretical results with experiments on synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2022-02-10T20:44:44Z)
• Subspace Learning for Personalized Federated Optimization [7.475183117508927]
  We propose a method to address the problem of personalized learning in AI systems. We show that our method achieves consistent gains both in personalized and unseen client evaluation settings.
  arXiv  Detail & Related papers  (2021-09-16T00:03:23Z)
• Toward Understanding the Influence of Individual Clients in Federated Learning [52.07734799278535]
  Federated learning allows clients to jointly train a global model without sending their private data to a central server. We defined a new notion called em-Influence, quantify this influence over parameters, and proposed an effective efficient model to estimate this metric.
  arXiv  Detail & Related papers  (2020-12-20T14:34:36Z)
• 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.