Federated Mutual Learning

• URL: http://arxiv.org/abs/2006.16765v3
• Date: Thu, 17 Sep 2020 06:10:24 GMT
• Title: Federated Mutual Learning
• Authors: Tao Shen, Jie Zhang, Xinkang Jia, Fengda Zhang, Gang Huang, Pan Zhou, Kun Kuang, Fei Wu, Chao Wu
• Abstract summary: Federated Mutual Leaning (FML) allows clients training a generalized model collaboratively and a personalized model independently. The experiments show that FML can achieve better performance than alternatives in typical Federated learning setting.
• Score: 65.46254760557073
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) enables collaboratively training deep learning models on decentralized data. However, there are three types of heterogeneities in FL setting bringing about distinctive challenges to the canonical federated learning algorithm (FedAvg). First, due to the Non-IIDness of data, the global shared model may perform worse than local models that solely trained on their private data; Second, the objective of center server and clients may be different, where center server seeks for a generalized model whereas client pursue a personalized model, and clients may run different tasks; Third, clients may need to design their customized model for various scenes and tasks; In this work, we present a novel federated learning paradigm, named Federated Mutual Leaning (FML), dealing with the three heterogeneities. FML allows clients training a generalized model collaboratively and a personalized model independently, and designing their private customized models. Thus, the Non-IIDness of data is no longer a bug but a feature that clients can be personally served better. The experiments show that FML can achieve better performance than alternatives in typical FL setting, and clients can be benefited from FML with different models and tasks.

Related papers

• Multi-Level Additive Modeling for Structured Non-IID Federated Learning [54.53672323071204]
  We train models organized in a multi-level structure, called Multi-level Additive Models (MAM)'', for better knowledge-sharing across heterogeneous clients. In federated MAM (FeMAM), each client is assigned to at most one model per level and its personalized prediction sums up the outputs of models assigned to it across all levels. Experiments show that FeMAM surpasses existing clustered FL and personalized FL methods in various non-IID settings.
  arXiv  Detail & Related papers  (2024-05-26T07:54:53Z)
• MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes [49.22075916259368]
  In some real-world applications, data samples are usually distributed on local devices. In this paper, we focus on a special kind of Non-I.I.D. scene where clients own incomplete classes. Our proposed algorithm named MAP could simultaneously achieve the aggregation and personalization goals in FL.
  arXiv  Detail & Related papers  (2024-04-14T12:22:42Z)
• Client-supervised Federated Learning: Towards One-model-for-all Personalization [28.574858341430858]
  We propose a novel federated learning framework to learn only one robust global model to achieve competitive performance to those personalized models on unseen/test clients in the FL system. Specifically, we design a new Client-Supervised Federated Learning (FedCS) to unravel clients' bias on instances' latent representations so that the global model can learn both client-specific and client-agnostic knowledge.
  arXiv  Detail & Related papers  (2024-03-28T15:29:19Z)
• FAM: fast adaptive federated meta-learning [10.980548731600116]
  We propose a fast adaptive federated meta-learning (FAM) framework for collaboratively learning a single global model. A skeleton network is grown on each client to train a personalized model by learning additional client-specific parameters from local data. The personalized client models outperformed the locally trained models, demonstrating the efficacy of the FAM mechanism.
  arXiv  Detail & Related papers  (2023-08-26T22:54:45Z)
• PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learning [55.930403371398114]
  We propose a novel generative adversarial network (GAN) sharing and aggregation strategy for personalized learning (PFL) PFL-GAN addresses the client heterogeneity in different scenarios. More specially, we first learn the similarity among clients and then develop an weighted collaborative data aggregation. The empirical results through the rigorous experimentation on several well-known datasets demonstrate the effectiveness of PFL-GAN.
  arXiv  Detail & Related papers  (2023-08-23T22:38:35Z)
• Personalizing or Not: Dynamically Personalized Federated Learning with Incentives [37.42347737911428]
  We propose personalized federated learning (FL) for learning personalized models without sharing private data. We introduce the personalization rate, measured as the fraction of clients willing to train personalized models, into federated settings and propose DyPFL. This technique incentivizes clients to participate in personalizing local models while allowing the adoption of the global model when it performs better.
  arXiv  Detail & Related papers  (2022-08-12T09:51:20Z)
• No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices [79.16481453598266]
  We propose InclusiveFL, a client-inclusive federated learning method to handle this problem. The core idea of InclusiveFL is to assign models of different sizes to clients with different computing capabilities. We also propose an effective method to share the knowledge among multiple local models with different sizes.
  arXiv  Detail & Related papers  (2022-02-16T13:03:27Z)
• Federated Multi-Task Learning under a Mixture of Distributions [10.00087964926414]
  Federated Learning (FL) is a framework for on-device collaborative training of machine learning models. First efforts in FL focused on learning a single global model with good average performance across clients, but the global model may be arbitrarily bad for a given client. We study federated MTL under the flexible assumption that each local data distribution is a mixture of unknown underlying distributions.
  arXiv  Detail & Related papers  (2021-08-23T15:47:53Z)
• Personalized Federated Learning by Structured and Unstructured Pruning under Data Heterogeneity [3.291862617649511]
  We propose a new approach for obtaining a personalized model from a client-level objective. To realize this personalization, we leverage finding a small subnetwork for each client.
  arXiv  Detail & Related papers  (2021-05-02T22:10:46Z)
• Personalized Federated Learning with First Order Model Optimization [76.81546598985159]
  We propose an alternative to federated learning, where each client federates with other relevant clients to obtain a stronger model per client-specific objectives. We do not assume knowledge of underlying data distributions or client similarities, and allow each client to optimize for arbitrary target distributions of interest. Our method outperforms existing alternatives, while also enabling new features for personalized FL such as transfer outside of local data distributions.
  arXiv  Detail & Related papers  (2020-12-15T19:30:29Z)

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.