Efficient Fine-Tuning of Large Pre-Trained Models in Federated Learning via Low-Rank, Task-Specific Adapter Clustering

核心概念

The proposed FL-TAC algorithm enables efficient fine-tuning of large pre-trained models in federated learning by training low-rank, task-specific adapters on client devices and performing clustering-based aggregation on the server to facilitate knowledge exchange across tasks.

摘要

The paper addresses the challenge of efficiently fine-tuning large pre-trained models in federated learning (FL) environments, where communication overhead is a significant bottleneck. The key idea is to train low-rank, task-specific adapters on client devices and perform clustering-based aggregation on the server to facilitate knowledge exchange across tasks.
Specifically, the FL-TAC algorithm works as follows:

Each client trains a unique, low-rank adapter for each of its local tasks using the LoRA technique.
The server performs K-means clustering on the received adapters to group those corresponding to the same task.
The server then aggregates the adapters within each cluster to form the global task-specific adapters, which are sent back to the clients.

This approach reduces the communication cost compared to transmitting the entire pre-trained model, while also enabling effective task adaptation by leveraging task-specific adapters. Extensive experiments on various language and vision tasks demonstrate the effectiveness of FL-TAC in terms of both performance and communication efficiency compared to baseline methods.
The paper also provides insights into the evolution of task-specific adapters throughout the FL training process, highlighting how the clustering step can be utilized to enhance generalization across multiple downstream tasks in a distributed setting.

統計資料

The Databricks-Dolly-15k dataset contains 8 tasks, including Brainstorming, Summarization, Classification, Creative Writing, Information Extraction, Closed QA, Open QA, and General QA.
The GLUE datasets used are SST2, MRPC, QQP, QNLI, and RTE.
The image classification tasks are CIFAR-10 and CIFAR-100.

引述

"The proposed FL-TAC algorithm enables an efficient and effective FL framework, which is capable of accommodating various target tasks from image to text classification and generation, and outperforms single-adapter baselines in both performance and communication efficiency."
"The successful clustering of task-specific adapters provides insights into the evolution of trainable parameters during the FL training process, demonstrating how this property can be utilized for effective generalization across multiple downstream tasks in a distributed setting."

從以下內容提煉的關鍵洞見

FL-TAC: Enhanced Fine-Tuning in Federated Learning via Low-Rank, Task-Specific Adapter Clustering

by Siqi Ping,Yu... 於 arxiv.org 04-25-2024

https://arxiv.org/pdf/2404.15384.pdf

深入探究

How can the proposed FL-TAC algorithm be extended to handle dynamic task distributions, where the set of tasks changes over time

To handle dynamic task distributions where the set of tasks changes over time, the FL-TAC algorithm can be extended by incorporating a mechanism for adaptive clustering and adapter creation. This adaptation would involve continuously monitoring the task distribution across clients and dynamically updating the clustering algorithm to accommodate new tasks.
One approach could be to implement a reinforcement learning-based system that learns to adjust the clustering strategy based on the changing task landscape. By training the system to optimize a reward function that considers factors like task similarity, adapter performance, and communication efficiency, the algorithm can dynamically reorganize task clusters as new tasks emerge or existing tasks evolve.
Additionally, a meta-learning framework could be employed to enable the FL-TAC algorithm to quickly adapt to new tasks by leveraging past experiences and knowledge gained from previous task distributions. This meta-learning approach would involve training the system on a diverse set of task distributions to develop a robust adaptation mechanism that can efficiently handle dynamic changes in task sets.

What are the potential limitations of the K-means clustering approach used in the server-side aggregation, and how could alternative clustering methods be explored to further improve the performance

The K-means clustering approach used in the server-side aggregation of the FL-TAC algorithm may have limitations when dealing with complex and non-linear task distributions. One potential limitation is the sensitivity of K-means to outliers, which can lead to suboptimal cluster assignments and impact the overall performance of the algorithm.
To address these limitations and potentially improve performance, alternative clustering methods could be explored. One option is to consider density-based clustering algorithms like DBSCAN (Density-Based Spatial Clustering of Applications with Noise), which are more robust to outliers and can identify clusters of varying shapes and sizes. By incorporating DBSCAN or similar density-based methods, the FL-TAC algorithm may achieve more accurate and flexible clustering results.
Another approach could involve using hierarchical clustering techniques such as agglomerative clustering, which can capture hierarchical relationships between task-specific adapters and provide a more nuanced understanding of the task similarities within the federated learning environment. By leveraging hierarchical clustering, the FL-TAC algorithm may achieve a more granular and informative clustering structure that enhances knowledge transfer and aggregation among related tasks.

Given the insights into the evolution of task-specific adapters, how could the FL-TAC framework be adapted to enable more efficient knowledge transfer between related tasks in a federated setting

To enable more efficient knowledge transfer between related tasks in a federated setting, the FL-TAC framework can be adapted by incorporating a task similarity metric into the clustering and aggregation process. By quantifying the similarity between tasks based on factors such as data distribution, model performance, and adapter characteristics, the FL-TAC algorithm can prioritize the exchange of information between closely related tasks.
One approach to enhancing knowledge transfer is to implement a task embedding mechanism that maps tasks into a continuous vector space based on their similarities. By embedding tasks in a shared latent space, the FL-TAC algorithm can identify task relationships and facilitate more targeted knowledge transfer between tasks with high similarity scores. This embedding approach can improve the efficiency of information exchange and aggregation, leading to enhanced performance across related tasks.
Furthermore, a transfer learning strategy could be integrated into the FL-TAC framework to leverage knowledge from tasks with abundant data to improve the performance of tasks with limited data. By transferring knowledge and model parameters from tasks with similar characteristics or objectives, the FL-TAC algorithm can enhance generalization and adaptation capabilities, enabling more efficient learning across a diverse set of tasks in a federated learning environment.