Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection

• URL: http://arxiv.org/abs/2401.16685v1
• Date: Tue, 30 Jan 2024 02:16:19 GMT
• Title: Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection
• Authors: Liangqi Yuan, Dong-Jun Han, Su Wang, Devesh Upadhyay, Christopher G. Brinton
• Abstract summary: Multimodal Federated learning (FL) aims to enrich model training in FL settings where clients are collecting measurements across multiple modalities. Key challenges to multimodal FL remain unaddressed, particularly in heterogeneous network settings. We propose mmFedMC, a new FL methodology that can tackle the above-mentioned challenges in multimodal settings.
• Score: 14.261582708240407
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal federated learning (FL) aims to enrich model training in FL settings where clients are collecting measurements across multiple modalities. However, key challenges to multimodal FL remain unaddressed, particularly in heterogeneous network settings where: (i) the set of modalities collected by each client will be diverse, and (ii) communication limitations prevent clients from uploading all their locally trained modality models to the server. In this paper, we propose multimodal Federated learning with joint Modality and Client selection (mmFedMC), a new FL methodology that can tackle the above-mentioned challenges in multimodal settings. The joint selection algorithm incorporates two main components: (a) A modality selection methodology for each client, which weighs (i) the impact of the modality, gauged by Shapley value analysis, (ii) the modality model size as a gauge of communication overhead, against (iii) the frequency of modality model updates, denoted recency, to enhance generalizability. (b) A client selection strategy for the server based on the local loss of modality model at each client. Experiments on five real-world datasets demonstrate the ability of mmFedMC to achieve comparable accuracy to several baselines while reducing the communication overhead by over 20x. A demo video of our methodology is available at https://liangqiy.com/mmfedmc/.

Related papers

• FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts [4.412721048192925]
  We present FedMoE, the efficient personalized Federated Learning framework to address data heterogeneity. FedMoE is composed of two fine-tuning stages. In the first stage, FedMoE simplifies the problem by conducting a search based on observed activation patterns. In the second stage, these submodels are distributed to clients for further training and returned for server aggregating.
  arXiv  Detail & Related papers  (2024-08-21T03:16:12Z)
• Leveraging Foundation Models for Multi-modal Federated Learning with Incomplete Modality [41.79433449873368]
  We propose a novel multi-modal federated learning method, Federated Multi-modal contrastiVe training with Pre-trained completion (FedMVP) FedMVP integrates the large-scale pre-trained models to enhance the federated training. We demonstrate that the model achieves superior performance over two real-world image-text classification datasets.
  arXiv  Detail & Related papers  (2024-06-16T19:18:06Z)
• 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)
• FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communication [11.254610576923204]
  We propose Federated Multimodal Fusion learning with Selective modality communication (FedMFS) Key idea is the introduction of a modality selection criterion for each device, which weighs (i) the impact of the modality, gauged by Shapley value analysis, against (ii) the modality model size as a gauge for communication overhead. Experiments on the real-world ActionSense dataset demonstrate the ability of FedMFS to achieve comparable accuracy to several baselines while reducing the communication overhead by over 4x.
  arXiv  Detail & Related papers  (2023-10-10T22:23:27Z)
• FM-ViT: Flexible Modal Vision Transformers for Face Anti-Spoofing [88.6654909354382]
  We present a pure transformer-based framework, dubbed the Flexible Modal Vision Transformer (FM-ViT) for face anti-spoofing. FM-ViT can flexibly target any single-modal (i.e., RGB) attack scenarios with the help of available multi-modal data. Experiments demonstrate that the single model trained based on FM-ViT can not only flexibly evaluate different modal samples, but also outperforms existing single-modal frameworks by a large margin.
  arXiv  Detail & Related papers  (2023-05-05T04:28:48Z)
• Multimodal Federated Learning via Contrastive Representation Ensemble [17.08211358391482]
  Federated learning (FL) serves as a privacy-conscious alternative to centralized machine learning. Existing FL methods all rely on model aggregation on single modality level. We propose Contrastive Representation Ensemble and Aggregation for Multimodal FL (CreamFL)
  arXiv  Detail & Related papers  (2023-02-17T14:17:44Z)
• 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)
• Multi-Model Federated Learning [8.629912408966145]
  We extend federated learning to the setting where multiple unrelated models are trained simultaneously. Every client is able to train any one of M models at a time and the server maintains a model for each of the M models which is typically a suitably averaged version of the model computed by the clients. We propose multiple policies for assigning learning tasks to clients over time. In the first policy, we extend the widely studied FedAvg to multi-model learning by allotting models to clients in an i.i.d. In addition, we propose two new policies for client selection in a multi-model setting which make decisions based on current
  arXiv  Detail & Related papers  (2022-01-07T18:24:23Z)
• Federated Noisy Client Learning [105.00756772827066]
  Federated learning (FL) collaboratively aggregates a shared global model depending on multiple local clients. Standard FL methods ignore the noisy client issue, which may harm the overall performance of the aggregated model. We propose Federated Noisy Client Learning (Fed-NCL), which is a plug-and-play algorithm and contains two main components.
  arXiv  Detail & Related papers  (2021-06-24T11:09:17Z)
• Low-Latency Federated Learning over Wireless Channels with Differential Privacy [142.5983499872664]
  In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server. In this paper, we aim to minimize FL training delay over wireless channels, constrained by overall training performance as well as each client's differential privacy (DP) requirement.
  arXiv  Detail & Related papers  (2021-06-20T13:51:18Z)
• 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.