# Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare arXiv: https://arxiv.org/abs/2405.06784 ยท HTML: https://mlsanity.com/paper/2405.06784/ ## Key facts - Cited by 12 papers in the map (data through 14 Oct 2026). - Builds on 139 of its 282 references that are in the map. - First posted on arXiv 10 May 2024. ## Builds on (most cited first, 30 of 139) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019) - [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021) - [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020) - [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015) - [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023) - [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023) - [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016) - [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019) - [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021) - [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022) - [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022) - [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016) - [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022) - [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020) - [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013) - [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019) - [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021) - [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017) - [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021) - [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013) ## Built on by (most cited first, 12 of 12) - [The Future of Continual Learning in the Era of Foundation Models: Three Key Directions](https://mlsanity.com/paper/2506.03320/) (2025) - [Foundation Models in Medical Imaging: A Review and Outlook](https://mlsanity.com/paper/2506.09095/) (2025) - [Evolution of meta's llama models and parameter-efficient fine-tuning of large language models: a survey](https://mlsanity.com/paper/2510.12178/) (2025) - [Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MR](https://mlsanity.com/paper/2506.05683/) (2025) - [Design and implementation of a distributed security threat detection system integrating federated learning and multimodal LLM](https://mlsanity.com/paper/2502.17763/) (2025) - [Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors](https://mlsanity.com/paper/2505.00580/) (2025) - [DINO-MX: A Modular & Flexible Framework for Self-Supervised Learning](https://mlsanity.com/paper/2511.01610/) (2025) - [Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning](https://mlsanity.com/paper/2507.00423/) (2025) - [Personalization of Large Foundation Models for Health Interventions](https://mlsanity.com/paper/2601.03482/) (2026) - [Remapping and navigation of an embedding space via error minimization: a fundamental organizational principle of cognition in natural and artificial systems](https://mlsanity.com/paper/2601.14096/) (2026) - [Diagnostic Accuracy of Open-Source Vision-Language Models on Diverse Medical Imaging Tasks](https://mlsanity.com/paper/2508.01016/) (2025) - [Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges](https://mlsanity.com/paper/2509.07946/) (2025) ## Abstract (arXiv) This survey explores the transformative impact of foundation models (FMs) in artificial intelligence, focusing on their integration with federated learning (FL) for advancing biomedical research. Foundation models such as ChatGPT, LLaMa, and CLIP, which are trained on vast datasets through methods including unsupervised pretraining, self-supervised learning, instructed fine-tuning, and reinforcement learning from human feedback, represent significant advancements in machine learning. These models, with their ability to generate coherent text and realistic images, are crucial for biomedical applications that require processing diverse data forms such as clinical reports, diagnostic images, and multimodal patient interactions. The incorporation of FL with these sophisticated models presents a promising strategy to harness their analytical power while safeguarding the privacy of sensitive medical data. This approach not only enhances the capabilities of FMs in medical diagnostics and personalized treatment but also addresses critical concerns about data privacy and security in healthcare. This survey reviews the current applications of FMs in federated settings, underscores the challenges, and identifies future research directions including scaling FMs, managing data diversity, and enhancing communication efficiency within FL frameworks. The objective is to encourage further research into the combined potential of FMs and FL, laying the groundwork for groundbreaking healthcare innovations.