Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

• URL: http://arxiv.org/abs/2408.05678v1
• Date: Sun, 11 Aug 2024 02:59:11 GMT
• Title: Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data
• Authors: Ji Liu, Juncheng Jia, Hong Zhang, Yuhui Yun, Leye Wang, Yang Zhou, Huaiyu Dai, Dejing Dou,
• Abstract summary: Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. We propose a new FL framework, FedDUMAP, to leverage the shared insensitive data on the server and the distributed data in edge devices. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches.
• Score: 59.6985168241067
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this paper, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent trade-off between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller).

Related papers

• Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models [43.26028399395612]
  We propose a Fisher Information-based Efficient Curriculum Federated Learning framework (FibecFed) with two novel methods. First, we propose a fisher information-based method to adaptively sample data within each device to improve the effectiveness of the FL fine-tuning process. Second, we dynamically select the proper layers for global aggregation and sparse parameters for local update with LoRA.
  arXiv  Detail & Related papers  (2024-09-30T18:12:18Z)
• AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices [61.66943750584406]
  We propose an Asynchronous Efficient Decentralized FL framework, i.e., AEDFL, in heterogeneous environments. First, we propose an asynchronous FL system model with an efficient model aggregation method for improving the FL convergence. Second, we propose a dynamic staleness-aware model update approach to achieve superior accuracy. Third, we propose an adaptive sparse training method to reduce communication and computation costs without significant accuracy degradation.
  arXiv  Detail & Related papers  (2023-12-18T05:18:17Z)
• Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization [71.87335804334616]
  Federated learning (FL) is a promising paradigm to enable collaborative model training with decentralized data. The training process of Large Language Models (LLMs) generally incurs the update of significant parameters. This paper proposes an efficient partial prompt tuning approach to improve performance and efficiency simultaneously.
  arXiv  Detail & Related papers  (2023-10-23T16:37:59Z)
• FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data [54.81695390763957]
  Federated learning is an emerging distributed machine learning method. We propose a heterogeneous local variant of AMSGrad, named FedLALR, in which each client adjusts its learning rate. We show that our client-specified auto-tuned learning rate scheduling can converge and achieve linear speedup with respect to the number of clients.
  arXiv  Detail & Related papers  (2023-09-18T12:35:05Z)
• FedDCT: A Dynamic Cross-Tier Federated Learning Framework in Wireless Networks [5.914766366715661]
  Federated Learning (FL) trains a global model across devices without exposing local data. resource heterogeneity and inevitable stragglers in wireless networks severely impact the efficiency and accuracy of FL training. We propose a novel Dynamic Cross-Tier Federated Learning framework (FedDCT)
  arXiv  Detail & Related papers  (2023-07-10T08:54:07Z)
• FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server [64.94942635929284]
  Federated Learning (FL) suffers from two critical challenges, i.e., limited computational resources and low training efficiency. We propose a novel FL framework, FedDUAP, to exploit the insensitive data on the server and the decentralized data in edge devices. By integrating the two original techniques together, our proposed FL model, FedDUAP, significantly outperforms baseline approaches in terms of accuracy (up to 4.8% higher), efficiency (up to 2.8 times faster), and computational cost (up to 61.9% smaller)
  arXiv  Detail & Related papers  (2022-04-25T10:00:00Z)
• Adaptive Serverless Learning [114.36410688552579]
  We propose a novel adaptive decentralized training approach, which can compute the learning rate from data dynamically. Our theoretical results reveal that the proposed algorithm can achieve linear speedup with respect to the number of workers. To reduce the communication-efficient overhead, we further propose a communication-efficient adaptive decentralized training approach.
  arXiv  Detail & Related papers  (2020-08-24T13:23:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.