F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics (2411.11912v2)

Abstract: Effective training of large Vision-LLMs (VLMs) on resource-constrained client devices in Federated Learning (FL) requires the usage of parameter-efficient fine-tuning (PEFT) strategies. To this end, we demonstrate the impact of two factors \textit{viz.}, client-specific layer importance score that selects the most important VLM layers for fine-tuning and inter-client layer diversity score that encourages diverse layer selection across clients for optimal VLM layer selection. We first theoretically motivate and leverage the principal eigenvalue magnitude of layerwise Neural Tangent Kernels and show its effectiveness as client-specific layer importance score. Next, we propose a novel layer updating strategy dubbed F$^3$OCUS that jointly optimizes the layer importance and diversity factors by employing a data-free, multi-objective, meta-heuristic optimization on the server. We explore 5 different meta-heuristic algorithms and compare their effectiveness for selecting model layers and adapter layers towards PEFT-FL. Furthermore, we release a new MedVQA-FL dataset involving overall 707,962 VQA triplets and 9 modality-specific clients and utilize it to train and evaluate our method. Overall, we conduct more than 10,000 client-level experiments on 6 Vision-Language FL task settings involving 58 medical image datasets and 4 different VLM architectures of varying sizes to demonstrate the effectiveness of the proposed method.

• The paper introduces F3OCUS, a framework that computes client-specific layer importance using LNTK to optimize federated fine-tuning.
• It employs a data-free multi-objective meta-heuristic strategy to balance layer importance scores and reduce update variance across clients.
• Extensive evaluations on over 10,000 client experiments, including the Ultra-MedVQA dataset, demonstrate improved convergence and state-of-the-art accuracy.

Federated Fine-Tuning with F$^{3}$OCUS: A Multi-Objective Approach for Vision-LLMs

The paper "F$^3$OCUS - Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics" addresses a critical challenge in the field of Federated Learning (FL), specifically focusing on the resource-constrained deployment of Vision-LLMs (VLMs). The authors propose a novel framework named F$^{3}$OCUS designed to optimize the fine-tuning of large VLMs across distributed client devices while ensuring the efficient utilization of available resources.

Key Contributions and Methods

The paper introduces a flexible strategy that encompasses both client-level and server-level optimizations to address the intricacies of parameter-efficient fine-tuning (PEFT). The primary contributions can be categorized as follows:

1. Client-level Layer Importance: The paper innovatively utilizes Layerwise Neural Tangent Kernel (LNTK) to compute a client-specific layer importance score based on the principal eigenvalue—arguably one of the distinguishing features of this research. These scores guide the selection of layers most relevant to each client's data distribution.
2. Server-level Optimization: At the heart of the framework is a data-free, multi-objective meta-heuristic optimization process run on the server. It aims to balance two objectives: maximizing the cumulative importance scores and minimizing the variance of layer selection across clients. The authors employ five distinct meta-heuristic algorithms, including Genetic Algorithm and Swarm Particle Optimization, to navigate this complex optimization landscape.
3. Ultra-MedVQA Dataset: The paper also contributes significantly to the community by introducing the Ultra-MedVQA dataset, the largest of its kind for medical visual question answering tasks. This dataset is leveraged to validate the proposed method in a practical setting.

Experimental Evaluation and Results

The authors conduct an extensive series of experiments, encompassing over 10,000 client-level evaluations. They test their methodology across six different vision-language FL settings using four VLM architectures, showcasing the robustness and adaptability of the proposed approach. The key outcomes include:

• Demonstrated improvements in model convergence rates and task performance across diverse datasets and configurations.
• Performance evaluations indicate that F$^{3}$OCUS can achieve superior accuracy compared to traditional and state-of-the-art FL methods, reinforcing its efficacy in resource-heterogeneous environments.
• The framework exhibits notable adaptability, handling scenarios characterized by domain gaps, modality gaps, and statistical heterogeneities effectively.

Implications and Future Directions

The implications of this research are substantial, offering a pragmatic approach to deploying complex VLMs in real-world, decentralized environments like healthcare, where data privacy and compute limitations are critical considerations. By cleverly employing the concept of LNTK and leveraging server-based optimization without the need for data, the work defines a path forward for scalable and efficient model fine-tuning in federated settings.

From a theoretical standpoint, the convergence analysis provided elucidates the impact of client-specific selection noise and inter-client diversity, contributing to the foundational understanding of FL dynamics.

Looking to the future, this work could catalyze further research into meta-heuristic strategies for FL, potentially inspiring analogous techniques across different model architectures and domains. Moreover, the Ultra-MedVQA dataset will likely serve as a benchmark for subsequent innovations in medical AI research.

In summary, the F$^{3}$OCUS framework represents a significant step forward in federated machine learning, driving improvements in performance while respecting the constraints inherent to distributed systems. This paper will undoubtedly influence future strategies for large-scale, privacy-preserving AI applications.