Interpretable Data Fusion for Distributed Learning: A Representative Approach via Gradient Matching

• URL: http://arxiv.org/abs/2405.03782v1
• Date: Mon, 6 May 2024 18:21:41 GMT
• Title: Interpretable Data Fusion for Distributed Learning: A Representative Approach via Gradient Matching
• Authors: Mengchen Fan, Baocheng Geng, Keren Li, Xueqian Wang, Pramod K. Varshney,
• Abstract summary: This paper introduces a representative-based approach for distributed learning that transforms multiple raw data points into a virtual representation. It achieves this by condensing extensive datasets into digestible formats, thus fostering intuitive human-machine interactions.
• Score: 19.193379036629167
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper introduces a representative-based approach for distributed learning that transforms multiple raw data points into a virtual representation. Unlike traditional distributed learning methods such as Federated Learning, which do not offer human interpretability, our method makes complex machine learning processes accessible and comprehensible. It achieves this by condensing extensive datasets into digestible formats, thus fostering intuitive human-machine interactions. Additionally, this approach maintains privacy and communication efficiency, and it matches the training performance of models using raw data. Simulation results show that our approach is competitive with or outperforms traditional Federated Learning in accuracy and convergence, especially in scenarios with complex models and a higher number of clients. This framework marks a step forward in integrating human intuition with machine intelligence, which potentially enhances human-machine learning interfaces and collaborative efforts.

Related papers

• Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient Similarity to Reduce Communication in Distributed and Federated Learning [50.382793324572845]
  Distributed computing involves communication between devices, which requires solving two key problems: efficiency and privacy. In this paper, we analyze a new method that incorporates the ideas of using data similarity and clients sampling. To address privacy concerns, we apply the technique of additional noise and analyze its impact on the convergence of the proposed method.
  arXiv  Detail & Related papers  (2024-09-22T00:49:10Z)
• Personalized Federated Learning with Contextual Modulation and Meta-Learning [2.7716102039510564]
  Federated learning has emerged as a promising approach for training machine learning models on decentralized data sources. We propose a novel framework that combines federated learning with meta-learning techniques to enhance both efficiency and generalization capabilities.
  arXiv  Detail & Related papers  (2023-12-23T08:18:22Z)
• Reinforcement Learning Based Multi-modal Feature Fusion Network for Novel Class Discovery [47.28191501836041]
  In this paper, we employ a Reinforcement Learning framework to simulate the cognitive processes of humans. We also deploy a Member-to-Leader Multi-Agent framework to extract and fuse features from multi-modal information. We demonstrate the performance of our approach in both the 3D and 2D domains by employing the OS-MN40, OS-MN40-Miss, and Cifar10 datasets.
  arXiv  Detail & Related papers  (2023-08-26T07:55:32Z)
• ALP: Action-Aware Embodied Learning for Perception [60.64801970249279]
  We introduce Action-Aware Embodied Learning for Perception (ALP) ALP incorporates action information into representation learning through a combination of optimizing a reinforcement learning policy and an inverse dynamics prediction objective. We show that ALP outperforms existing baselines in several downstream perception tasks.
  arXiv  Detail & Related papers  (2023-06-16T21:51:04Z)
• Personalizing Federated Learning with Over-the-Air Computations [84.8089761800994]
  Federated edge learning is a promising technology to deploy intelligence at the edge of wireless networks in a privacy-preserving manner. Under such a setting, multiple clients collaboratively train a global generic model under the coordination of an edge server. This paper presents a distributed training paradigm that employs analog over-the-air computation to address the communication bottleneck.
  arXiv  Detail & Related papers  (2023-02-24T08:41:19Z)
• A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison [0.6299766708197883]
  Pervasive computing promotes the installation of connected devices in our living spaces in order to provide services. Two major developments have gained significant momentum recently: an advanced use of edge resources and the integration of machine learning techniques for engineering applications. We propose a novel aggregation algorithm, termed FedDist, which is able to modify its model architecture by identifying dissimilarities between specific neurons amongst the clients.
  arXiv  Detail & Related papers  (2021-10-19T19:43:28Z)
• Exploiting Shared Representations for Personalized Federated Learning [54.65133770989836]
  We propose a novel federated learning framework and algorithm for learning a shared data representation across clients and unique local heads for each client. Our algorithm harnesses the distributed computational power across clients to perform many local-updates with respect to the low-dimensional local parameters for every update of the representation. This result is of interest beyond federated learning to a broad class of problems in which we aim to learn a shared low-dimensional representation among data distributions.
  arXiv  Detail & Related papers  (2021-02-14T05:36:25Z)
• Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations [6.9485501711137525]
  We explore an alternative federated learning system that enables integration of dimensionality reduced representations of distributed data prior to a supervised learning task. We compare the performance of this approach on image classification tasks to three alternative frameworks: centralized machine learning, individual machine learning, and Federated Averaging. Our results show that our approach can achieve similar accuracy as Federated Averaging and performs better than Federated Averaging in a small-user setting.
  arXiv  Detail & Related papers  (2020-11-13T08:12:00Z)
• Relation-Guided Representation Learning [53.60351496449232]
  We propose a new representation learning method that explicitly models and leverages sample relations. Our framework well preserves the relations between samples. By seeking to embed samples into subspace, we show that our method can address the large-scale and out-of-sample problem.
  arXiv  Detail & Related papers  (2020-07-11T10:57:45Z)
• Human Trajectory Forecasting in Crowds: A Deep Learning Perspective [89.4600982169]
  We present an in-depth analysis of existing deep learning-based methods for modelling social interactions. We propose two knowledge-based data-driven methods to effectively capture these social interactions. We develop a large scale interaction-centric benchmark TrajNet++, a significant yet missing component in the field of human trajectory forecasting.
  arXiv  Detail & Related papers  (2020-07-07T17:19:56Z)

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.