A Web-Based Solution for Federated Learning with LLM-Based Automation

• URL: http://arxiv.org/abs/2408.13010v1
• Date: Fri, 23 Aug 2024 11:57:02 GMT
• Title: A Web-Based Solution for Federated Learning with LLM-Based Automation
• Authors: Chamith Mawela, Chaouki Ben Issaid, Mehdi Bennis,
• Abstract summary: Federated Learning (FL) offers a promising approach for collaborative machine learning across distributed devices. We develop a user-friendly web application supporting the federated averaging (FedAvg) algorithm. We explore intent-based automation in FL using a fine-tuned Language Model (LLM) trained on a tailored dataset.
• Score: 34.756818299081736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) offers a promising approach for collaborative machine learning across distributed devices. However, its adoption is hindered by the complexity of building reliable communication architectures and the need for expertise in both machine learning and network programming. This paper presents a comprehensive solution that simplifies the orchestration of FL tasks while integrating intent-based automation. We develop a user-friendly web application supporting the federated averaging (FedAvg) algorithm, enabling users to configure parameters through an intuitive interface. The backend solution efficiently manages communication between the parameter server and edge nodes. We also implement model compression and scheduling algorithms to optimize FL performance. Furthermore, we explore intent-based automation in FL using a fine-tuned Language Model (LLM) trained on a tailored dataset, allowing users to conduct FL tasks using high-level prompts. We observe that the LLM-based automated solution achieves comparable test accuracy to the standard web-based solution while reducing transferred bytes by up to 64% and CPU time by up to 46% for FL tasks. Also, we leverage the neural architecture search (NAS) and hyperparameter optimization (HPO) using LLM to improve the performance. We observe that by using this approach test accuracy can be improved by 10-20% for the carried out FL tasks.

Related papers

• SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational Overhead [75.87007729801304]
  SpaFL: a communication-efficient FL framework is proposed to optimize sparse model structures with low computational overhead. Experiments show that SpaFL improves accuracy while requiring much less communication and computing resources compared to sparse baselines.
  arXiv  Detail & Related papers  (2024-06-01T13:10:35Z)
• Communication Efficient ConFederated Learning: An Event-Triggered SAGA Approach [67.27031215756121]
  Federated learning (FL) is a machine learning paradigm that targets model training without gathering the local data over various data sources. Standard FL, which employs a single server, can only support a limited number of users, leading to degraded learning capability. In this work, we consider a multi-server FL framework, referred to as emphConfederated Learning (CFL) in order to accommodate a larger number of users.
  arXiv  Detail & Related papers  (2024-02-28T03:27:10Z)
• 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)
• FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy [7.963276533979389]
  Federated Learning (FL) achieves great popularity in the Internet of Things (IoT) We present FLrce, an efficient FL framework with a relationship-based client selection and early-stopping strategy. Experiment results show that, compared with existing efficient FL frameworks, FLrce improves the computation and communication efficiency by at least 30% and 43% respectively.
  arXiv  Detail & Related papers  (2023-10-15T10:13:44Z)
• Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly [62.473245910234304]
  This paper takes a hardware-centric approach to explore how Large Language Models can be brought to modern edge computing systems. We provide a micro-level hardware benchmark, compare the model FLOP utilization to a state-of-the-art data center GPU, and study the network utilization in realistic conditions.
  arXiv  Detail & Related papers  (2023-10-04T20:27:20Z)
• Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence [83.58839320635956]
  Federated Learning (FL) can be used in mobile edge networks to train machine learning models in a distributed manner. Recent FL has been interpreted within a Model-Agnostic Meta-Learning (MAML) framework, which brings FL significant advantages in fast adaptation and convergence over heterogeneous datasets. This paper addresses how much benefit MAML brings to FL and how to maximize such benefit over mobile edge networks.
  arXiv  Detail & Related papers  (2023-03-23T02:42:10Z)
• Federated Learning with Flexible Control [30.65854375019346]
  Federated learning (FL) enables distributed model training from local data collected by users. In distributed systems with constrained resources and potentially high dynamics, e.g., mobile edge networks, the efficiency of FL is an important problem. We propose FlexFL - an FL algorithm with multiple options that can be adjusted flexibly.
  arXiv  Detail & Related papers  (2022-12-16T14:21:29Z)
• A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning [17.55163940659976]
  Federated learning (FL) is a training technique that enables client devices to jointly learn a shared model. We design an efficient FL framework which jointly optimize model accuracy, processing latency and communication efficiency. Experiments show that FedMarl can significantly improve model accuracy with much lower processing latency and communication cost.
  arXiv  Detail & Related papers  (2022-01-09T05:55:17Z)
• FedFog: Network-Aware Optimization of Federated Learning over Wireless Fog-Cloud Systems [40.421253127588244]
  Federated learning (FL) is capable of performing large distributed machine learning tasks across multiple edge users by periodically aggregating trained local parameters. We first propose an efficient FL algorithm (called FedFog) to perform the local aggregation of gradient parameters at fog servers and global training update at the cloud.
  arXiv  Detail & Related papers  (2021-07-04T08:03:15Z)
• Delay Minimization for Federated Learning Over Wireless Communication Networks [172.42768672943365]
  The problem of delay computation for federated learning (FL) over wireless communication networks is investigated. A bisection search algorithm is proposed to obtain the optimal solution. Simulation results show that the proposed algorithm can reduce delay by up to 27.3% compared to conventional FL methods.
  arXiv  Detail & Related papers  (2020-07-05T19:00:07Z)

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.