Enhancing Efficiency in Multidevice Federated Learning through Data Selection

• URL: http://arxiv.org/abs/2211.04175v4
• Date: Wed, 10 Apr 2024 12:01:20 GMT
• Title: Enhancing Efficiency in Multidevice Federated Learning through Data Selection
• Authors: Fan Mo, Mohammad Malekzadeh, Soumyajit Chatterjee, Fahim Kawsar, Akhil Mathur,
• Abstract summary: Federated learning (FL) in multidevice environments creates new opportunities to learn from a vast and diverse amount of private data. In this paper, we develop an FL framework to incorporate on-device data selection on such constrained devices. We show that our framework achieves 19% higher accuracy and 58% lower latency; compared to the baseline FL without our implemented strategies.
• Score: 11.67484476827617
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Federated learning (FL) in multidevice environments creates new opportunities to learn from a vast and diverse amount of private data. Although personal devices capture valuable data, their memory, computing, connectivity, and battery resources are often limited. Since deep neural networks (DNNs) are the typical machine learning models employed in FL, there are demands for integrating ubiquitous constrained devices into the training process of DNNs. In this paper, we develop an FL framework to incorporate on-device data selection on such constrained devices, which allows partition-based training of a DNN through collaboration between constrained devices and resourceful devices of the same client. Evaluations on five benchmark DNNs and six benchmark datasets across different modalities show that, on average, our framework achieves ~19% higher accuracy and ~58% lower latency; compared to the baseline FL without our implemented strategies. We demonstrate the effectiveness of our FL framework when dealing with imbalanced data, client participation heterogeneity, and various mobility patterns. As a benchmark for the community, our code is available at https://github.com/dr-bell/data-centric-federated-learning

Related papers

• Enhancing Federated Learning Convergence with Dynamic Data Queue and Data Entropy-driven Participant Selection [13.825031686864559]
  Federated Learning (FL) is a decentralized approach for collaborative model training on edge devices. We present a method to improve convergence in FL by creating a global subset of data on the server and dynamically distributing it across devices. Our approach results in a substantial accuracy boost of approximately 5% for the MNIST dataset, around 18% for CIFAR-10, and 20% for CIFAR-100 with a 10% global subset of data, outperforming the state-of-the-art (SOTA) aggregation algorithms.
  arXiv  Detail & Related papers  (2024-10-23T11:47:04Z)
• FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based Human Activity Recognition [0.0]
  This paper explores Federated Transfer Learning in a Multi-Task manner for both sensor-based human activity recognition and device position identification tasks. The OpenHAR framework is used to train the models, which contains ten smaller datasets. By utilizing transfer learning and training a task-specific and personalized federated model, we obtained a similar accuracy with training each client individually and higher accuracy than a fully centralized approach.
  arXiv  Detail & Related papers  (2023-11-13T21:31:07Z)
• 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)
• FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systems [61.335229621081346]
  Federated Learning (FL) has become a viable technique for realizing privacy-enhancing distributed deep learning on the network edge. In this paper, we propose FLEdge, which complements existing FL benchmarks by enabling a systematic evaluation of client capabilities.
  arXiv  Detail & Related papers  (2023-06-08T13:11:20Z)
• Collaborative Learning with a Drone Orchestrator [79.75113006257872]
  A swarm of intelligent wireless devices train a shared neural network model with the help of a drone. The proposed framework achieves a significant speedup in training, leading to an average 24% and 87% saving in the drone hovering time.
  arXiv  Detail & Related papers  (2023-03-03T23:46:25Z)
• Scalable Collaborative Learning via Representation Sharing [53.047460465980144]
  Federated learning (FL) and Split Learning (SL) are two frameworks that enable collaborative learning while keeping the data private (on device) In FL, each data holder trains a model locally and releases it to a central server for aggregation. In SL, the clients must release individual cut-layer activations (smashed data) to the server and wait for its response (during both inference and back propagation). In this work, we present a novel approach for privacy-preserving machine learning, where the clients collaborate via online knowledge distillation using a contrastive loss.
  arXiv  Detail & Related papers  (2022-11-20T10:49:22Z)
• ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme [37.099990745974196]
  We introduce an On-Demand-FL, a client deployment approach for federated learning. We make use of containerization technology such as Docker to build efficient environments. The Genetic algorithm (GA) is used to solve the multi-objective optimization problem.
  arXiv  Detail & Related papers  (2022-11-05T13:41:19Z)
• Federated Learning and Meta Learning: Approaches, Applications, and Directions [94.68423258028285]
  In this tutorial, we present a comprehensive review of FL, meta learning, and federated meta learning (FedMeta) Unlike other tutorial papers, our objective is to explore how FL, meta learning, and FedMeta methodologies can be designed, optimized, and evolved, and their applications over wireless networks.
  arXiv  Detail & Related papers  (2022-10-24T10:59:29Z)
• Online Data Selection for Federated Learning with Limited Storage [53.46789303416799]
  Federated Learning (FL) has been proposed to achieve distributed machine learning among networked devices. The impact of on-device storage on the performance of FL is still not explored. In this work, we take the first step to consider the online data selection for FL with limited on-device storage.
  arXiv  Detail & Related papers  (2022-09-01T03:27:33Z)
• Federated Split GANs [12.007429155505767]
  We propose an alternative approach to train ML models in user's devices themselves. We focus on GANs (generative adversarial networks) and leverage their inherent privacy-preserving attribute. Our system preserves data privacy, keeps a short training time, and yields same accuracy of model training in unconstrained devices.
  arXiv  Detail & Related papers  (2022-07-04T23:53:47Z)
• FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity [56.82825745165945]
  Federated Learning (FL) enables training a global model without sharing the decentralized raw data stored on multiple devices to protect data privacy. We propose a hierarchical synchronous FL framework, i.e., FedHiSyn, to tackle the problems of straggler effects and outdated models. We evaluate the proposed framework based on MNIST, EMNIST, CIFAR10 and CIFAR100 datasets and diverse heterogeneous settings of devices.
  arXiv  Detail & Related papers  (2022-06-21T17:23:06Z)
• Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point [51.47520726446029]
  cooperative edge learning (CE-FL) is a distributed machine learning architecture. We model the processes taken during CE-FL, and conduct analytical training. We show the effectiveness of our framework with the data collected from a real-world testbed.
  arXiv  Detail & Related papers  (2022-03-26T00:41:57Z)
• FLAME: Federated Learning Across Multi-device Environments [9.810211000961647]
  Federated Learning (FL) enables distributed training of machine learning models while keeping personal data on user devices private. We propose FLAME, a user-centered FL training approach to counter statistical and system heterogeneity in multi-device environments. Our experiment results show that FLAME outperforms various baselines by 4.8-33.8% higher F-1 score, 1.02-2.86x greater energy efficiency, and up to 2.02x speedup in convergence.
  arXiv  Detail & Related papers  (2022-02-17T22:23:56Z)
• DQRE-SCnet: A novel hybrid approach for selecting users in Federated Learning with Deep-Q-Reinforcement Learning based on Spectral Clustering [1.174402845822043]
  Machine learning models based on sensitive data in the real-world promise advances in areas ranging from medical screening to disease outbreaks, agriculture, industry, defense science, and more. In many applications, learning participant communication rounds benefit from collecting their own private data sets, teaching detailed machine learning models on the real data, and sharing the benefits of using these models. Due to existing privacy and security concerns, most people avoid sensitive data sharing for training. Without each user demonstrating their local data to a central server, Federated Learning allows various parties to train a machine learning algorithm on their shared data jointly.
  arXiv  Detail & Related papers  (2021-11-07T15:14:29Z)
• Adaptive Dynamic Pruning for Non-IID Federated Learning [3.8666113275834335]
  Federated Learning(FL) has emerged as a new paradigm of training machine learning models without sacrificing data security and privacy. We present an adaptive pruning scheme for edge devices in an FL system, which applies dataset-aware dynamic pruning for inference acceleration on Non-IID datasets.
  arXiv  Detail & Related papers  (2021-06-13T05:27:43Z)
• Federated Learning-based Active Authentication on Mobile Devices [98.23904302910022]
  User active authentication on mobile devices aims to learn a model that can correctly recognize the enrolled user based on device sensor information. We propose a novel user active authentication training, termed as Federated Active Authentication (FAA) We show that existing FL/SL methods are suboptimal for FAA as they rely on the data to be distributed homogeneously.
  arXiv  Detail & Related papers  (2021-04-14T22:59:08Z)

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.