A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison

• URL: http://arxiv.org/abs/2110.10223v1
• Date: Tue, 19 Oct 2021 19:43:28 GMT
• Title: A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison
• Authors: Sannara Ek, Fran\c{c}ois Portet, Philippe Lalanda, German Vega
• Abstract summary: 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.
• Score: 0.6299766708197883
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. This evolution raises major challenges, in particular related to the appropriate distribution of computing elements along an edge-to-cloud continuum. About this, Federated Learning has been recently proposed for distributed model training in the edge. The principle of this approach is to aggregate models learned on distributed clients in order to obtain a new, more general model. The resulting model is then redistributed to clients for further training. To date, the most popular federated learning algorithm uses coordinate-wise averaging of the model parameters for aggregation. However, it has been shown that this method is not adapted in heterogeneous environments where data is not identically and independently distributed (non-iid). This corresponds directly to some pervasive computing scenarios where heterogeneity of devices and users challenges machine learning with the double objective of generalization and personalization. In this paper, we propose a novel aggregation algorithm, termed FedDist, which is able to modify its model architecture (here, deep neural network) by identifying dissimilarities between specific neurons amongst the clients. This permits to account for clients' specificity without impairing generalization. Furthermore, we define a complete method to evaluate federated learning in a realistic way taking generalization and personalization into account. Using this method, FedDist is extensively tested and compared with three state-of-the-art federated learning algorithms on the pervasive domain of Human Activity Recognition with smartphones.

Related papers

• Personalized Federated Learning via Sequential Layer Expansion in Representation Learning [0.0]
  Federated learning ensures the privacy of clients by conducting distributed training on individual client devices and sharing only the model weights with a central server. We propose a new representation learning-based approach that suggests decoupling the entire deep learning model into more densely divided parts with the application of suitable scheduling methods.
  arXiv  Detail & Related papers  (2024-04-27T06:37:19Z)
• Tackling Computational Heterogeneity in FL: A Few Theoretical Insights [68.8204255655161]
  We introduce and analyse a novel aggregation framework that allows for formalizing and tackling computational heterogeneous data. Proposed aggregation algorithms are extensively analyzed from a theoretical, and an experimental prospective.
  arXiv  Detail & Related papers  (2023-07-12T16:28:21Z)
• 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)
• Evaluation and comparison of federated learning algorithms for Human Activity Recognition on smartphones [0.5039813366558306]
  Federated Learning (FL) has been introduced as a new machine learning paradigm enhancing the use of local devices. In this paper, we propose a new FL algorithm, termed FedDist, which can modify models during training by identifying dissimilarities between neurons among the clients. Results have shown the ability of FedDist to adapt to heterogeneous data and the capability of FL to deal with asynchronous situations.
  arXiv  Detail & Related papers  (2022-10-30T18:47:23Z)
• FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks [21.613436984547917]
  We propose a novel personalized federated learning method called federated classifier averaging (FedClassAvg) FedClassAvg aggregates weights as an agreement on decision boundaries on feature spaces. We demonstrate it outperforms the current state-of-the-art algorithms on heterogeneous personalized federated learning tasks.
  arXiv  Detail & Related papers  (2022-10-25T08:32:08Z)
• Federated Learning Aggregation: New Robust Algorithms with Guarantees [63.96013144017572]
  Federated learning has been recently proposed for distributed model training at the edge. This paper presents a complete general mathematical convergence analysis to evaluate aggregation strategies in a federated learning framework. We derive novel aggregation algorithms which are able to modify their model architecture by differentiating client contributions according to the value of their losses.
  arXiv  Detail & Related papers  (2022-05-22T16:37:53Z)
• FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for Federated Learning on Non-IID Data [69.0785021613868]
  Federated learning is a distributed machine learning approach which enables a shared server model to learn by aggregating the locally-computed parameter updates with the training data from spatially-distributed client silos. We propose the Federated Invariant Learning Consistency (FedILC) approach, which leverages the gradient covariance and the geometric mean of Hessians to capture both inter-silo and intra-silo consistencies. This is relevant to various fields such as medical healthcare, computer vision, and the Internet of Things (IoT)
  arXiv  Detail & Related papers  (2022-05-19T03:32:03Z)
• An Expectation-Maximization Perspective on Federated Learning [75.67515842938299]
  Federated learning describes the distributed training of models across multiple clients while keeping the data private on-device. In this work, we view the server-orchestrated federated learning process as a hierarchical latent variable model where the server provides the parameters of a prior distribution over the client-specific model parameters. We show that with simple Gaussian priors and a hard version of the well known Expectation-Maximization (EM) algorithm, learning in such a model corresponds to FedAvg, the most popular algorithm for the federated learning setting.
  arXiv  Detail & Related papers  (2021-11-19T12:58:59Z)
• A Personalized Federated Learning Algorithm: an Application in Anomaly Detection [0.6700873164609007]
  Federated Learning (FL) has recently emerged as a promising method to overcome data privacy and transmission issues. In FL, datasets collected from different devices or sensors are used to train local models (clients) each of which shares its learning with a centralized model (server) This paper proposes a novel Personalized FedAvg (PC-FedAvg) which aims to control weights communication and aggregation augmented with a tailored learning algorithm to personalize the resulting models at each client.
  arXiv  Detail & Related papers  (2021-11-04T04:57:11Z)
• Clustered Federated Learning via Generalized Total Variation Minimization [83.26141667853057]
  We study optimization methods to train local (or personalized) models for local datasets with a decentralized network structure. Our main conceptual contribution is to formulate federated learning as total variation minimization (GTV) Our main algorithmic contribution is a fully decentralized federated learning algorithm.
  arXiv  Detail & Related papers  (2021-05-26T18:07:19Z)

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.