FLAMMABLE: A Multi-Model Federated Learning Framework with Multi-Model Engagement and Adaptive Batch Sizes

• URL: http://arxiv.org/abs/2510.10380v1
• Date: Sun, 12 Oct 2025 00:38:10 GMT
• Title: FLAMMABLE: A Multi-Model Federated Learning Framework with Multi-Model Engagement and Adaptive Batch Sizes
• Authors: Shouxu Lin, Zimeng Pan, Yuhang Yao, Haeyoung Noh, Pei Zhang, Carlee Joe-Wong,
• Abstract summary: Multi-Model Federated Learning (MMFL) is an emerging direction in Federated Learning (FL)<n>We propose FLAMMABLE, a comprehensive MMFL training framework.<n>We show that FLAMMABLE boosts the MMFL time-to-accuracy performance by 1.1$sim$10.0$times$ while improving the final model accuracy by 1.3$sim$5.4% compared to several known baselines.
• Score: 26.562420671856568
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-Model Federated Learning (MMFL) is an emerging direction in Federated Learning (FL) where multiple models are trained in parallel, generally on various datasets. Optimizing the models' accuracies and training times in the MMFL setting requires adapting to data and system heterogeneity across clients as in single-model FL; these challenges are amplified in the MMFL setting due to additional heterogeneity across models. Neither existing solutions nor na\"ive extensions of single-model FL frameworks efficiently address these challenges. To bridge this gap, we propose FLAMMABLE, a comprehensive MMFL training framework. FLAMMABLE optimizes model training by intelligently adapting client batch sizes while engaging them to train multiple carefully chosen models, depending on their system capabilities, in each training round. To evaluate FLAMMABLE, we develop the first benchmark platform for the MMFL setting, which may enable future reproducible MMFL research. Extensive evaluations on multiple datasets and models show that FLAMMABLE boosts the MMFL time-to-accuracy performance by 1.1$\sim$10.0$\times$ while improving the final model accuracy by 1.3$\sim$5.4\% compared to several known baselines.

Related papers

• Towards Optimal Heterogeneous Client Sampling in Multi-Model Federated Learning [22.787635207005884]
  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.
  arXiv  Detail & Related papers  (2025-04-07T14:43:17Z)
• 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)
• 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)
• A Survey on Efficient Federated Learning Methods for Foundation Model Training [62.473245910234304]
  Federated Learning (FL) has become an established technique to facilitate privacy-preserving collaborative training across a multitude of clients. In the wake of Foundation Models (FM), the reality is different for many deep learning applications. We discuss the benefits and drawbacks of parameter-efficient fine-tuning (PEFT) for FL applications.
  arXiv  Detail & Related papers  (2024-01-09T10:22:23Z)
• NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients [44.89061671579694]
  Federated learning (FL) enables distributed training while preserving data privacy, but stragglers-slow or incapable clients-can significantly slow down the total training time and degrade performance. We propose nested federated learning (NeFL), a framework that efficiently divides deep neural networks into submodels using both depthwise and widthwise scaling. NeFL achieves performance gain, especially for the worst-case submodel compared to baseline approaches.
  arXiv  Detail & Related papers  (2023-08-15T13:29:14Z)
• Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization [107.72786199113183]
  Federated learning (FL) provides a distributed learning framework for multiple participants to collaborate learning without sharing raw data. In this paper, we propose a novel Split-Mix FL strategy for heterogeneous participants that, once training is done, provides in-situ customization of model sizes and robustness.
  arXiv  Detail & Related papers  (2022-03-18T04:58:34Z)
• Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performance [4.689140226545214]
  Federated Learning (FL), Split Learning (SL), and SplitFed Learning (SFL) are three recent developments in distributed machine learning. This paper studies SFL without client-side model synchronization. It provides only 1%-2% better accuracy than Multi-head Split Learning on the MNIST test set.
  arXiv  Detail & Related papers  (2021-09-19T22:57:23Z)
• Achieving Model Fairness in Vertical Federated Learning [47.8598060954355]
  Vertical federated learning (VFL) enables multiple enterprises possessing non-overlapped features to strengthen their machine learning models without disclosing their private data and model parameters. VFL suffers from fairness issues, i.e., the learned model may be unfairly discriminatory over the group with sensitive attributes. We propose a fair VFL framework to tackle this problem.
  arXiv  Detail & Related papers  (2021-09-17T04:40:11Z)
• PFL-MoE: Personalized Federated Learning Based on Mixture of Experts [1.8757823231879849]
  Federated learning (FL) avoids data sharing among training nodes so as to protect data privacy. PFL-MoE is a generic approach and can be instantiated by integrating existing PFL algorithms. We demonstrate the effectiveness of PFL-MoE by training the LeNet-5 and VGG-16 models on the Fashion-MNIST datasets.
  arXiv  Detail & Related papers  (2020-12-31T12:51:14Z)
• 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)
• Ensemble Distillation for Robust Model Fusion in Federated Learning [72.61259487233214]
  Federated Learning (FL) is a machine learning setting where many devices collaboratively train a machine learning model. In most of the current training schemes the central model is refined by averaging the parameters of the server model and the updated parameters from the client side. We propose ensemble distillation for model fusion, i.e. training the central classifier through unlabeled data on the outputs of the models from the clients.
  arXiv  Detail & Related papers  (2020-06-12T14:49:47Z)

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.