mlsanityData to 14 Oct 2026

FHBench: Towards Efficient and Personalized Federated Learning for Multimodal Healthcare

arXiv 2504.10817 ↗First posted 15 Apr 2025cs.LG cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Federated Learning (FL) has emerged as an effective solution for multi-institutional collaborations without sharing patient data, offering a range of methods tailored for diverse applications. However, real-world medical datasets are often multimodal, and computational resources are limited, posing significant challenges for existing FL approaches. Recognizing these limitations, we developed the Federated Healthcare Benchmark(FHBench), a benchmark specifically designed from datasets derived from real-world healthcare applications. FHBench encompasses critical diagnostic tasks across domains such as the nervous, cardiovascular, and respiratory systems and general pathology, providing comprehensive support for multimodal healthcare evaluations and filling a significant gap in existing benchmarks. Building on FHBench, we introduced Efficient Personalized Federated Learning with Adaptive LoRA(EPFL), a personalized FL framework that demonstrates superior efficiency and effectiveness across various healthcare modalities. Our results highlight the robustness of FHBench as a benchmarking tool and the potential of EPFL as an innovative approach to advancing healthcare-focused FL, addressing key limitations of existing methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 31 of its 42 references that are in the map. First posted on arXiv 15 Apr 2025.

Builds on

31 of 42 references.

References · 31

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  4. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  5. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  6. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
  7. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  8. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  9. 2018LEAF: A Benchmark for Federated Settingscited by 792
  10. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 690
  11. 2024MOMENT: A Family of Open Time-series Foundation Modelscited by 482
  12. 2020Adaptive Personalized Federated Learningcited by 358
19 more
  1. 2020MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysiscited by 206
  2. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  3. 2024OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learningcited by 128
  4. 2017PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstractscited by 124
  5. 2022FedALA: Adaptive Local Aggregation for Personalized Federated Learningcited by 122
  6. 2021FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Taskscited by 85
  7. 2022When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methodscited by 75
  8. 2023Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimizationcited by 65
  9. 2024FDLoRA: Personalized Federated Learning of Large Language Model via Dual LoRA Tuningcited by 59
  10. 2021Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learningcited by 53
  11. 2024Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classificationcited by 53
  12. 2023FedMultimodal: A Benchmark For Multimodal Federated Learningcited by 51
  13. 2022Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approachescited by 39
  14. 2022MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcarecited by 37
  15. 2021Personalized Federated Learning with Adaptive Batchnorm for Healthcarecited by 26
  16. 2025Ten Challenging Problems in Federated Foundation Modelscited by 20
  17. 2022Industry-Scale Orchestrated Federated Learning for Drug Discoverycited by 19
  18. 2024FLoRA: Enhancing Vision-Language Models with Parameter-Efficient Federated Learningcited by 16
  19. 2022Pretraining Respiratory Sound Representations using Metadata and Contrastive Learningcited by 15

Built on by

Not cited yet.