Decentralised and collaborative machine learning framework for IoT

• URL: http://arxiv.org/abs/2312.12190v1
• Date: Tue, 19 Dec 2023 14:25:41 GMT
• Title: Decentralised and collaborative machine learning framework for IoT
• Authors: Mart\'in Gonz\'alez-Soto and Rebeca P. D\'iaz-Redondo and Manuel Fern\'andez-Veiga and Bruno Rodr\'iguez-Castro and Ana Fern\'andez-Vilas
• Abstract summary: We propose a decentralised and collaborative machine learning framework specially oriented to resource-constrained devices. First, an incremental learning algorithm based on prototypes that was specifically implemented to work in low-performance computing elements. Second, two random-based protocols to exchange the local models among the computing elements in the network.
• Score: 0.32985979395737774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Decentralised machine learning has recently been proposed as a potential solution to the security issues of the canonical federated learning approach. In this paper, we propose a decentralised and collaborative machine learning framework specially oriented to resource-constrained devices, usual in IoT deployments. With this aim we propose the following construction blocks. First, an incremental learning algorithm based on prototypes that was specifically implemented to work in low-performance computing elements. Second, two random-based protocols to exchange the local models among the computing elements in the network. Finally, two algorithmics approaches for prediction and prototype creation. This proposal was compared to a typical centralized incremental learning approach in terms of accuracy, training time and robustness with very promising results.

Related papers

• Initialisation and Network Effects in Decentralised Federated Learning [1.5961625979922607]
  Decentralised federated learning enables collaborative training of individual machine learning models on a distributed network of communicating devices. This approach avoids central coordination, enhances data privacy and eliminates the risk of a single point of failure. We propose a strategy for uncoordinated initialisation of the artificial neural networks based on the distribution of eigenvector centralities of the underlying communication network.
  arXiv  Detail & Related papers  (2024-03-23T14:24:36Z)
• Asynchronous Parallel Incremental Block-Coordinate Descent for Decentralized Machine Learning [55.198301429316125]
  Machine learning (ML) is a key technique for big-data-driven modelling and analysis of massive Internet of Things (IoT) based intelligent and ubiquitous computing. For fast-increasing applications and data amounts, distributed learning is a promising emerging paradigm since it is often impractical or inefficient to share/aggregate data. This paper studies the problem of training an ML model over decentralized systems, where data are distributed over many user devices.
  arXiv  Detail & Related papers  (2022-02-07T15:04:15Z)
• 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)
• Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networks [88.15736037284408]
  We study a new model of multiple federated learning services at the multi-access edge computing server. We propose a joint resource optimization and hyper-learning rate control problem, namely MS-FEDL. Our simulation results demonstrate the convergence performance of our proposed algorithms.
  arXiv  Detail & Related papers  (2020-11-25T01:29:41Z)
• Decentralized Deep Learning using Momentum-Accelerated Consensus [15.333413663982874]
  We consider the problem of decentralized deep learning where multiple agents collaborate to learn from a distributed dataset. We propose and analyze a novel decentralized deep learning algorithm where the agents interact over a fixed communication topology. Our algorithm is based on the heavy-ball acceleration method used in gradient-based protocol.
  arXiv  Detail & Related papers  (2020-10-21T17:39:52Z)
• A Low Complexity Decentralized Neural Net with Centralized Equivalence using Layer-wise Learning [49.15799302636519]
  We design a low complexity decentralized learning algorithm to train a recently proposed large neural network in distributed processing nodes (workers) In our setup, the training data is distributed among the workers but is not shared in the training process due to privacy and security concerns. We show that it is possible to achieve equivalent learning performance as if the data is available in a single place.
  arXiv  Detail & Related papers  (2020-09-29T13:08:12Z)
• 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)
• F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning [110.35516334788687]
  Decentralized multi-agent reinforcement learning algorithms are sometimes unpractical in complicated applications. We propose a flexible fully decentralized actor-critic MARL framework, which can handle large-scale general cooperative multi-agent setting. Our framework can achieve scalability and stability for large-scale environment and reduce information transmission.
  arXiv  Detail & Related papers  (2020-04-17T14:56:29Z)
• Decentralized MCTS via Learned Teammate Models [89.24858306636816]
  We present a trainable online decentralized planning algorithm based on decentralized Monte Carlo Tree Search. We show that deep learning and convolutional neural networks can be employed to produce accurate policy approximators.
  arXiv  Detail & Related papers  (2020-03-19T13:10:20Z)
• Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks [8.456633924613456]
  Federated learning (FL) is emerging as a new paradigm to train machine learning models in distributed systems. The paper proposes a fully distributed (or server-less) learning approach: the proposed FL algorithms leverage the cooperation of devices that perform data operations inside the network. The approach lays the groundwork for integration of FL within 5G and beyond networks characterized by decentralized connectivity and computing.
  arXiv  Detail & Related papers  (2019-12-27T15:16:04Z)

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.