Federated Transfer Learning with Task Personalization for Condition Monitoring in Ultrasonic Metal Welding

• URL: http://arxiv.org/abs/2404.13278v1
• Date: Sat, 20 Apr 2024 05:31:59 GMT
• Title: Federated Transfer Learning with Task Personalization for Condition Monitoring in Ultrasonic Metal Welding
• Authors: Ahmadreza Eslaminia, Yuquan Meng, Klara Nahrstedt, Chenhui Shao,
• Abstract summary: This paper presents a Transfer Learning with. Federated Task Task architecture (FTLTP) that provides data capabilities in distributed distributed learning framework. The FTL-TP framework is readily to various other manufacturing applications.
• Score: 3.079885946230076
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ultrasonic metal welding (UMW) is a key joining technology with widespread industrial applications. Condition monitoring (CM) capabilities are critically needed in UMW applications because process anomalies significantly deteriorate the joining quality. Recently, machine learning models emerged as a promising tool for CM in many manufacturing applications due to their ability to learn complex patterns. Yet, the successful deployment of these models requires substantial training data that may be expensive and time-consuming to collect. Additionally, many existing machine learning models lack generalizability and cannot be directly applied to new process configurations (i.e., domains). Such issues may be potentially alleviated by pooling data across manufacturers, but data sharing raises critical data privacy concerns. To address these challenges, this paper presents a Federated Transfer Learning with Task Personalization (FTL-TP) framework that provides domain generalization capabilities in distributed learning while ensuring data privacy. By effectively learning a unified representation from feature space, FTL-TP can adapt CM models for clients working on similar tasks, thereby enhancing their overall adaptability and performance jointly. To demonstrate the effectiveness of FTL-TP, we investigate two distinct UMW CM tasks, tool condition monitoring and workpiece surface condition classification. Compared with state-of-the-art FL algorithms, FTL-TP achieves a 5.35%--8.08% improvement of accuracy in CM in new target domains. FTL-TP is also shown to perform excellently in challenging scenarios involving unbalanced data distributions and limited client fractions. Furthermore, by implementing the FTL-TP method on an edge-cloud architecture, we show that this method is both viable and efficient in practice. The FTL-TP framework is readily extensible to various other manufacturing applications.

Related papers

• Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
  Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and. Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting. LLMs to downstream tasks. We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T10:48:20Z)
• Lightweight Industrial Cohorted Federated Learning for Heterogeneous Assets [0.0]
  Federated Learning (FL) is the most widely adopted collaborative learning approach for training decentralized Machine Learning (ML) models. However, since great data similarity or homogeneity is taken for granted in all FL tasks, FL is still not specifically designed for the industrial setting. We propose a Lightweight Industrial Cohorted FL (LICFL) algorithm that uses model parameters for cohorting without any additional on-edge (clientlevel) computations and communications.
  arXiv  Detail & Related papers  (2024-07-25T12:48:56Z)
• A Survey on Efficient Federated Learning Methods for Foundation Model Training [62.473245910234304]
  Federated Learning (FL) has become an established technique to facilitate privacy-preserving collaborative training across a multitude of clients. In the wake of Foundation Models (FM), the reality is different for many deep learning applications. We discuss the benefits and drawbacks of parameter-efficient fine-tuning (PEFT) for FL applications.
  arXiv  Detail & Related papers  (2024-01-09T10:22:23Z)
• Adaptive Model Pruning and Personalization for Federated Learning over Wireless Networks [72.59891661768177]
  Federated learning (FL) enables distributed learning across edge devices while protecting data privacy. We consider a FL framework with partial model pruning and personalization to overcome these challenges. This framework splits the learning model into a global part with model pruning shared with all devices to learn data representations and a personalized part to be fine-tuned for a specific device.
  arXiv  Detail & Related papers  (2023-09-04T21:10:45Z)
• FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning [70.38817963253034]
  This paper first discusses these challenges of federated fine-tuning LLMs, and introduces our package FS-LLM as a main contribution. We provide comprehensive federated parameter-efficient fine-tuning algorithm implementations and versatile programming interfaces for future extension in FL scenarios. We conduct extensive experiments to validate the effectiveness of FS-LLM and benchmark advanced LLMs with state-of-the-art parameter-efficient fine-tuning algorithms in FL settings.
  arXiv  Detail & Related papers  (2023-09-01T09:40:36Z)
• Challenges and Opportunities of Using Transformer-Based Multi-Task Learning in NLP Through ML Lifecycle: A Survey [0.6240603866868214]
  Multi-Task Learning (MTL) has emerged as a promising approach to improve efficiency and performance through joint training. We discuss the challenges and opportunities of using MTL approaches throughout typical machine learning lifecycle phases. We believe it would be practical to have a model that can handle both MTL and continual learning.
  arXiv  Detail & Related papers  (2023-08-16T09:11:00Z)
• SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models [28.764782216513037]
  Federated Learning (FL) can benefit from distributed and private data of the FL edge clients for fine-tuning. We propose a method called SLoRA, which overcomes the key limitations of LoRA in high heterogeneous data scenarios. Our experimental results demonstrate that SLoRA achieves performance comparable to full fine-tuning.
  arXiv  Detail & Related papers  (2023-08-12T10:33:57Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• Federated Learning for Physical Layer Design [38.46522285374866]
  Federated learning (FL) has been proposed recently as a distributed learning scheme. FL is more communication-efficient and privacy-preserving than centralized learning (CL) This article discusses the recent advances in FL-based training for physical layer design problems.
  arXiv  Detail & Related papers  (2021-02-23T16:22:53Z)
• EasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP Applications [65.87067607849757]
  EasyTransfer is a platform to develop deep Transfer Learning algorithms for Natural Language Processing (NLP) applications. EasyTransfer supports various NLP models in the ModelZoo, including mainstream PLMs and multi-modality models. EasyTransfer is currently deployed at Alibaba to support a variety of business scenarios.
  arXiv  Detail & Related papers  (2020-11-18T18:41:27Z)
• Ternary Compression for Communication-Efficient Federated Learning [17.97683428517896]
  Federated learning provides a potential solution to privacy-preserving and secure machine learning. We propose a ternary federated averaging protocol (T-FedAvg) to reduce the upstream and downstream communication of federated learning systems. Our results show that the proposed T-FedAvg is effective in reducing communication costs and can even achieve slightly better performance on non-IID data.
  arXiv  Detail & Related papers  (2020-03-07T11:55:34Z)

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.