Multi-Level Additive Modeling for Structured Non-IID Federated Learning

• URL: http://arxiv.org/abs/2405.16472v1
• Date: Sun, 26 May 2024 07:54:53 GMT
• Title: Multi-Level Additive Modeling for Structured Non-IID Federated Learning
• Authors: Shutong Chen, Tianyi Zhou, Guodong Long, Jie Ma, Jing Jiang, Chengqi Zhang,
• Abstract summary: 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.
• Score: 54.53672323071204
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The primary challenge in Federated Learning (FL) is to model non-IID distributions across clients, whose fine-grained structure is important to improve knowledge sharing. For example, some knowledge is globally shared across all clients, some is only transferable within a subgroup of clients, and some are client-specific. To capture and exploit this structure, we train models organized in a multi-level structure, called ``Multi-level Additive Models (MAM)'', for better knowledge-sharing across heterogeneous clients and their personalization. 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. For the top level, FeMAM trains one global model shared by all clients as FedAvg. For every mid-level, it learns multiple models each assigned to a subgroup of clients, as clustered FL. Every bottom-level model is trained for one client only. In the training objective, each model aims to minimize the residual of the additive predictions by the other models assigned to each client. To approximate the arbitrary structure of non-IID across clients, FeMAM introduces more flexibility and adaptivity to FL by incrementally adding new models to the prediction of each client and reassigning another if necessary, automatically optimizing the knowledge-sharing structure. Extensive experiments show that FeMAM surpasses existing clustered FL and personalized FL methods in various non-IID settings. Our code is available at https://github.com/shutong043/FeMAM.

Related papers

• Personalized Hierarchical Split Federated Learning in Wireless Networks [24.664469755746463]
  We propose a personalized hierarchical split federated learning (PHSFL) algorithm that is specially designed to achieve better personalization performance. We first perform extensive theoretical analysis to understand the impact of model splitting and hierarchical model aggregations on the global model. Once the global model is trained, we fine-tune each client to obtain the personalized models.
  arXiv  Detail & Related papers  (2024-11-09T02:41:53Z)
• FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant Clients [13.98392319567057]
  Federated Learning (FL) is a distributed machine learning paradigm that achieves a globally robust model through decentralized computation and periodic model synthesis. Despite their wide adoption, existing FL and PFL works have yet to comprehensively address the class-imbalance issue. We propose FedReMa, an efficient PFL algorithm that can tackle class-imbalance by utilizing an adaptive inter-client co-learning approach.
  arXiv  Detail & Related papers  (2024-11-04T05:44:28Z)
• 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)
• Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting [76.64235084279292]
  One-shot Federated Learning (OFL) has become a promising learning paradigm, enabling the training of a global server model via a single communication round. We introduce a novel framework, Co-Boosting, in which synthesized data and the ensemble model mutually enhance each other progressively.
  arXiv  Detail & Related papers  (2024-02-23T03:15:10Z)
• 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)
• Visual Prompt Based Personalized Federated Learning [83.04104655903846]
  We propose a novel PFL framework for image classification tasks, dubbed pFedPT, that leverages personalized visual prompts to implicitly represent local data distribution information of clients. Experiments on the CIFAR10 and CIFAR100 datasets show that pFedPT outperforms several state-of-the-art (SOTA) PFL algorithms by a large margin in various settings.
  arXiv  Detail & Related papers  (2023-03-15T15:02:15Z)
• Personalized Federated Learning with Multi-branch Architecture [0.0]
  Federated learning (FL) enables multiple clients to collaboratively train models without requiring clients to reveal their raw data to each other. We propose a new PFL method (pFedMB) using multi-branch architecture, which achieves personalization by splitting each layer of a neural network into multiple branches and assigning client-specific weights to each branch. We experimentally show that pFedMB performs better than the state-of-the-art PFL methods using the CIFAR10 and CIFAR100 datasets.
  arXiv  Detail & Related papers  (2022-11-15T06:30:57Z)
• 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)
• Federated Mutual Learning [65.46254760557073]
  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.
  arXiv  Detail & Related papers  (2020-06-27T09:35:03Z)

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.