Federated Fine-Tuning (FFT)

• Federated Fine-Tuning (FFT) is a collaborative paradigm for adapting large pre-trained models across distributed client data under privacy and resource constraints.
• It employs techniques like model partitioning, communication-efficient fine-tuning, and robust aggregation to overcome hardware and bandwidth limitations.
• FFT enables real-world deployment of state-of-the-art models on mobile, edge, and IoT devices while maintaining data confidentiality and regulatory compliance.

Federated Fine-Tuning (FFT) refers to the collaborative adaptation of large-scale pre-trained models (such as foundation models, FMs) to downstream tasks by leveraging data that remains distributed across multiple clients, under strict privacy and resource constraints. FFT is a core paradigm for enabling the deployment and personalization of state-of-the-art models in real-world settings, where data cannot be centralized due to confidentiality, regulatory, or bandwidth limitations, and where client hardware (e.g., mobile, edge, IoT) is fundamentally resource-limited. Key technical avenues in FFT include sophisticated model partitioning; communication-efficient and parameter-efficient fine-tuning methods; robust aggregation strategies for system and data heterogeneity; secure and privacy-preserving protocols; and emerging approaches for continual, class-incremental, and hybrid model settings.

1. Model Partitioning and System Architecture

Modern FFT frameworks often employ model partitioning, or “split learning”, to reduce the on-device memory and computation load, while supporting end-to-end task adaptation.