When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

I. INTRODUCTION

• FL trains on distributed data sources and transfers model updates rather than raw data, enhancing data privacy.Source: §I · p. 1
• FL can expand FM data access, distribute computation, and broaden participation in FM development.Source: §I · p. 1
• FMs can give FL a pretrained starting point and provide synthetic data that enriches diversity, reduces overfitting, and can preserve privacy.Source: §I · pp. 1–2
• Multimodal FMs can annotate unlabeled data or filter low-quality data, reducing labeling costs for FL participants.Source: §I · p. 2
• The paper examines motivations, challenges, and opportunities in the interplay between FL and FMs to inform future research directions.Source: §I · p. 2

II. FEDERATED LEARNING FOR FOUNDATION MODELS

• The section introduces motivations for FM development.Source: §II · p. 2
• It also discusses challenges faced during FM development.Source: §II · p. 2
• The section explains how FL may serve as a solution and frames opportunities and future directions.Source: §II · p. 2

A. Motivations of FL for FM

• High-quality public training data is limited, and training on generated data risks model collapse and loss of the underlying data distribution.Source: A. Motivations of FL for FM · p. 2
• FL can draw on private distributed data, reduce reliance on unauthorized scraping, and support source tracing and selective unlearning.The paper also notes copyright and privacy concerns around publicly available data and centralized collection.Source: A. Motivations of FL for FM · pp. 2–3
• Pooling participants' computational power distributes FM training burden and may broaden access to FM development.Source: A. Motivations of FL for FM · p. 3
• FL can enable collaborative FM development beyond the affluent companies that currently dominate the resources and infrastructure.Source: A. Motivations of FL for FM · p. 3
• FL can incorporate incremental data into existing FMs, helping them adapt to continuously changing real-world data.Source: A. Motivations of FL for FM · p. 3
• Deploying FMs fully or partially at FL participants can reduce inference latency, service outages, and potential privacy problems compared with cloud APIs.Source: A. Motivations of FL for FM · p. 3

B. Challenges of Empowering FM with FL

• The illustrated approaches include FL training or fine-tuning with private client data, model-parallel training, and parameter-efficient local tuning with adapters or prompts.Source: Figure 5 · p. 4
• Large FM memory and compute demands make client hosting difficult, especially on cross-device clients with limited storage and processing capacity.Cross-silo participants also need substantial infrastructure for training or fine-tuning.Source: B. Challenges of Empowering FM with FL · pp. 3–4
• Transmitting large FM updates over limited or unreliable bandwidth can delay training, interrupt updates, and leave clients using stale model versions.Source: B. Challenges of Empowering FM with FL · p. 4
• FL for FMs must address privacy attacks, verify contributed-data rights, enable unlearning of problematic data, and protect against model poisoning and other security threats.Source: B. Challenges of Empowering FM with FL · p. 4
• FM collaboration requires incentives for participants with differing data and compute resources, plus fair decisions about model ownership, profits, and costs.Source: B. Challenges of Empowering FM with FL · pp. 4–5

Integrating FL into the Lifespan of FM

• FL can support FM pre-training with decentralized data, fine-tuning with incremental participant data, and inference through server and client models.The paper describes pre-training as supporting more generalized models, fine-tuning as keeping models up to date, and personalized client models as an option.Source: C. Opportunities and Future Directions · p. 5

Memory, Communication, and Computation Reduction

• Distributed learning and parameter-efficient adapters are proposed to reduce communication and computation, although fitting and fine-tuning FMs still require substantial resources.Model and pipeline parallelism distribute model processing across devices; adapter or LoRA methods freeze FM parameters and tune a smaller component.Source: C. Opportunities and Future Directions · p. 5

Prompt Tuning

• Prompt tuning offers a sharing alternative: participants tune lightweight prompts rather than sharing FM parameters, but device constraints and privacy concerns remain.The approaches discussed include assuming large FMs on user devices or relying on cloud APIs that require sending sensitive data.Source: C. Opportunities and Future Directions · p. 5

Model Compression

• Distillation and quantization can reduce FM size and computational requirements, and future work could combine compression with parameter-efficient tuning.The paper describes distillation as training a smaller model to replicate a larger model and quantization as reducing parameter precision.Source: C. Opportunities and Future Directions · p. 5

Designing FL System and Benchmark for FM

• FM-specific FL systems should address memory, communication, computation, scalability, efficiency, and privacy, while dedicated benchmarks can support fair evaluation.The paper notes that existing FL systems may not fully optimize for FM requirements.Source: C. Opportunities and Future Directions · pp. 5–6

Improving FM with Decentralized Data in FL

• Decentralized FL data may bridge FMs’ domain gaps and support continuous fine-tuning, while training FMs from scratch in cross-device FL remains challenging under current resource limits.The paper points to device improvements and model-size reduction as possible routes to future progress.Source: C. Opportunities and Future Directions · p. 6

Incentive Mechanisms

• FM participation calls for fair incentives that account for differing data and computing contributions and allocate deployment profits and costs equitably.The paper links equitable rewards to trust and ongoing engagement.Source: C. Opportunities and Future Directions · p. 6

Trustworthy FL for FM

• Trustworthy FL for FMs requires work on privacy, security, fairness, interpretability, intellectual property, ownership, memorization, and distributed deduplication.The paper specifically calls for a distributed deduplication mechanism in FL.Source: C. Opportunities and Future Directions · p. 6

• The section examines the motivations for using FMs in FL.Source: §III · p. 6
• It considers challenges that arise when bringing FMs into FL.Source: §III · p. 6
• It explores potential opportunities for using FMs in FL.Source: §III · p. 6

FM as an FL Starting Point

• Pre-trained FMs can initialize or teach FL models, transferring knowledge that may accelerate convergence, mitigate data-related performance issues, and reduce communication rounds.The figure also depicts using an FM, or a compressed or distilled FM, as an FL initialization.Source: A. Motivations of FM for FL · p. 6, Figure 6 · p. 7

Synthetic Data

• FM-generated synthetic data can address data scarcity, add diversity, support distillation, and supplement or replace sensitive training data.The paper describes generation by resource-equipped clients in cross-silo FL or by the server in cross-device FL, and notes potential risks from synthetic data in the broader discussion.Source: A. Motivations of FM for FL · pp. 6–7

FM as an Annotator

• FMs can annotate data with pseudo-labels, and combining those labels with real data in semi-supervised FL can further minimize degradation from noisy pseudo-labels.The paper describes annotation support for costly or challenging tasks including segmentation and object detection.Source: A. Motivations of FM for FL · p. 7

New Sharing Paradigm

• Prompt-based FL can aggregate and tune soft prompts while keeping pre-trained language models fixed, rather than sharing model parameters.The paper presents this as a communication-overhead-reducing sharing paradigm.Source: A. Motivations of FM for FL · p. 7

Multi-task and Multi-modality Capability

• Multi-task and multimodal FMs can be compressed or distilled for FL initialization or used to generate multimodal data and annotations across diverse tasks.The paper contrasts this with the difficulty of training a multi-task, multimodal model from scratch through FL.Source: A. Motivations of FM for FL · p. 7

Problematic Synthetic Data

• Synthetic data can inherit source bias, misinformation, or toxicity and may mismatch client distributions, introducing bias or noise and harming generalization.The paper also notes that overuse of synthetic data for LLMs may cause model collapse.Source: B. Challenges of Empowering FL with FM · pp. 7–8

Privacy and IP

• FM memorization can expose sensitive or proprietary training data or create copyright risks when generated data closely resembles its sources.Tracing synthetic-data lineage and verifying uniqueness are described as resource-intensive challenges for FL settings.Source: B. Challenges of Empowering FL with FM · p. 8

Misuse of Technology

• Using FMs in FL also raises misuse risks, including harmful content generation, misinformation, and privacy breaches.The paper calls for ethical guidelines and regulations to address these risks.Source: B. Challenges of Empowering FL with FM · p. 8

Unique Challenges in Adopting FM for FL

• Practical adoption challenges include scarce labels, changing environments, domain gaps, and limited white-box access; the paper notes a gap between research and deployment.Source: B. Challenges of Empowering FL with FM · p. 8

Legal Consideration and Responsible Usage of FM for FL

• Responsible FM use in FL calls for license and model assessment, including scrutiny of training-data copyright, privacy, bias, and security.The paper recommends transparent models with licenses such as CC BY 4.0 or MIT over less transparent models with vague licenses.Source: C. Opportunities and Future Directions · p. 8
• Future safeguards include checking synthetic data for privacy and IP risks, developing privacy-preserving synthesis, and involving law, ethics, and social sciences.Suggested checks include assessing similarities between synthetic and real-world data and improving privacy-assessment metrics.Source: C. Opportunities and Future Directions · p. 8

Personalization of FM for FL

• Personalization could tailor FMs to clients’ system constraints and data distributions, including by generating client-specific synthetic data.The figure distinguishes system heterogeneity (top) from data heterogeneity (bottom).Source: Figure 7 · p. 9, C. Opportunities and Future Directions · p. 9

Enhancing Robustness of FM for FL

• Synthetic-data mismatch can degrade FL performance, while poisoned FMs, Byzantine clients, adversarial examples, and FM-induced bias pose distinct robustness risks.The paper calls for better alignment of synthetic data with client distributions and techniques to address bias in augmentation and distillation.Source: C. Opportunities and Future Directions · pp. 8–9

Advanced Knowledge Distillation Techniques of FM for FL

• FM knowledge distillation for FL needs lower-cost methods and transfer approaches that also address fairness and privacy.The paper notes that high distillation costs can burden large-scale deployments and clients with limited computation.Source: C. Opportunities and Future Directions · pp. 9–10
• Black-box distillation can use FM outputs as pseudo-labels, but commonly relies on a same-domain public dataset that may be unavailable in sensitive settings.The paper proposes investigating data-free black-box distillation for transferring knowledge to FL clients.Source: C. Opportunities and Future Directions · p. 10

Real-world Applications of FM to FL

• Real-world FL could coordinate multiple FMs and explore industry-specific uses, including healthcare prediction and cross-modal learning.The section also identifies unlabeled data, continuous streams, domain discrepancies, and data and system heterogeneity as practical challenges.Source: C. Opportunities and Future Directions · p. 10
• Using FMs as initial models or synthetic-data generators could accelerate FL deployment, while future work should account for real-world constraints and requisites.The paper links these uses to faster convergence and better performance in FL implementations.Source: C. Opportunities and Future Directions · p. 10

• FL can support FM development through broader data access, shared computation, collaboration, continuous updates, and reduced service delay or downtime.Source: §IV · p. 10
• FMs can strengthen FL as training starting points and synthetic-data generators, while also offering new sharing paradigms and capabilities.Source: §IV · p. 10
• The paper highlights challenges and opportunities for future research on empowering each field with the strengths of the other.Source: §IV · p. 10