Towards Optimal Heterogeneous Client Sampling in Multi-Model Federated Learning

• URL: http://arxiv.org/abs/2504.05138v3
• Date: Mon, 21 Apr 2025 18:13:21 GMT
• Title: Towards Optimal Heterogeneous Client Sampling in Multi-Model Federated Learning
• Authors: Haoran Zhang, Zejun Gong, Zekai Li, Marie Siew, Carlee Joe-Wong, Rachid El-Azouzi,
• Abstract summary: Federated learning allows edge devices to collaboratively train models without sharing local data.<n>Clients may need to train multiple unrelated FL models, but communication constraints limit their ability to train all models simultaneously.<n>We propose MMFL-LVR, a loss-based sampling method that minimizes training variance while explicitly respecting communication limits at the server.
• Score: 22.787635207005884
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated learning (FL) allows edge devices to collaboratively train models without sharing local data. As FL gains popularity, clients may need to train multiple unrelated FL models, but communication constraints limit their ability to train all models simultaneously. While clients could train FL models sequentially, opportunistically having FL clients concurrently train different models -- termed multi-model federated learning (MMFL) -- can reduce the overall training time. Prior work uses simple client-to-model assignments that do not optimize the contribution of each client to each model over the course of its training. Prior work on single-model FL shows that intelligent client selection can greatly accelerate convergence, but na\"ive extensions to MMFL can violate heterogeneous resource constraints at both the server and the clients. In this work, we develop a novel convergence analysis of MMFL with arbitrary client sampling methods, theoretically demonstrating the strengths and limitations of previous well-established gradient-based methods. Motivated by this analysis, we propose MMFL-LVR, a loss-based sampling method that minimizes training variance while explicitly respecting communication limits at the server and reducing computational costs at the clients. We extend this to MMFL-StaleVR, which incorporates stale updates for improved efficiency and stability, and MMFL-StaleVRE, a lightweight variant suitable for low-overhead deployment. Experiments show our methods improve average accuracy by up to 19.1% over random sampling, with only a 5.4% gap from the theoretical optimum (full client participation).

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.<n>We propose Federated-Centric Adaptive Optimization, which is a class of novel federated optimization approaches.
  arXiv  Detail & Related papers  (2025-01-17T04:00:50Z)
• Over-the-Air Fair Federated Learning via Multi-Objective Optimization [52.295563400314094]
  We propose an over-the-air fair federated learning algorithm (OTA-FFL) to train fair FL models.<n>Experiments demonstrate the superiority of OTA-FFL in achieving fairness and robust performance.
  arXiv  Detail & Related papers  (2025-01-06T21:16:51Z)
• Embracing Federated Learning: Enabling Weak Client Participation via Partial Model Training [21.89214794178211]
  In Federated Learning (FL), clients may have weak devices that cannot train the full model or even hold it in their memory space. We propose EmbracingFL, a general FL framework that allows all available clients to join the distributed training. Our empirical study shows that EmbracingFL consistently achieves high accuracy as like all clients are strong, outperforming the state-of-the-art width reduction methods.
  arXiv  Detail & Related papers  (2024-06-21T13:19:29Z)
• Towards Instance-adaptive Inference for Federated Learning [80.38701896056828]
  Federated learning (FL) is a distributed learning paradigm that enables multiple clients to learn a powerful global model by aggregating local training. In this paper, we present a novel FL algorithm, i.e., FedIns, to handle intra-client data heterogeneity by enabling instance-adaptive inference in the FL framework. Our experiments show that our FedIns outperforms state-of-the-art FL algorithms, e.g., a 6.64% improvement against the top-performing method with less than 15% communication cost on Tiny-ImageNet.
  arXiv  Detail & Related papers  (2023-08-11T09:58:47Z)
• 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)
• Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networks [8.030674576024952]
  In federated learning (FL), distributed clients can collaboratively train a shared global model while retaining their own training data locally. In this paper, a joint optimization problem of client selection and resource allocation is formulated, aiming to minimize the total time consumption of each round in FL over a non-orthogonal multiple access (NOMA) enabled wireless network. In addition, a server-side artificial neural network (ANN) is proposed to predict the FL models of clients who are not selected at each round to further improve FL performance.
  arXiv  Detail & Related papers  (2023-04-18T13:58:16Z)
• Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach [20.300740276237523]
  Federated learning (FL) is an emerging machine learning (ML) paradigm used to train models across multiple nodes (i.e., clients) holding local data sets. We develop a novel algorithm to achieve this goal, dubbed Bandit Scheduling for FL (BSFL)
  arXiv  Detail & Related papers  (2023-03-18T09:45:58Z)
• FedCliP: Federated Learning with Client Pruning [3.796320380104124]
  Federated learning (FL) is a newly emerging distributed learning paradigm. One fundamental bottleneck in FL is the heavy communication overheads between the distributed clients and the central server. We propose FedCliP, the first communication efficient FL training framework from a macro perspective.
  arXiv  Detail & Related papers  (2023-01-17T09:15:37Z)
• Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learning [0.6882042556551609]
  Federated learning (FL) is a collaborative machine learning framework that requires different clients (e.g., Internet of Things devices) to participate in the machine learning model training process. Traditional FL process may suffer from the straggler problem in heterogeneous client settings. We propose a Semisynchronous-client Selection and mOdel aggregation aggregation for federated learNing (LESSON) method that allows all the clients to participate in the whole FL process but with different frequencies.
  arXiv  Detail & Related papers  (2022-10-19T05:59:22Z)
• 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)
• A Bayesian Federated Learning Framework with Online Laplace Approximation [144.7345013348257]
  Federated learning allows multiple clients to collaboratively learn a globally shared model. We propose a novel FL framework that uses online Laplace approximation to approximate posteriors on both the client and server side. We achieve state-of-the-art results on several benchmarks, clearly demonstrating the advantages of the proposed method.
  arXiv  Detail & Related papers  (2021-02-03T08:36:58Z)

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.