Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data

• URL: http://arxiv.org/abs/2409.12575v1
• Date: Thu, 19 Sep 2024 08:52:49 GMT
• Title: Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data
• Authors: Manuel Röder, Frank-Michael Schleif,
• Abstract summary: Federated learning allows multiple clients to collaboratively train a shared model while maintaining data privacy. The proposed framework utilizes transfer learning, pre-training deep neural networks on a central server, and fine-tuning on clients to enhance model accuracy and adaptability. Practical applications include Car2X event predictions, where a shared model is collectively trained to recognize traffic patterns, aiding in tasks such as traffic density assessment and accident detection.
• Score: 0.3683202928838613
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This extended abstract explores the integration of federated learning with deep transfer hashing for distributed prediction tasks, emphasizing resource-efficient client training from evolving data streams. Federated learning allows multiple clients to collaboratively train a shared model while maintaining data privacy - by incorporating deep transfer hashing, high-dimensional data can be converted into compact hash codes, reducing data transmission size and network loads. The proposed framework utilizes transfer learning, pre-training deep neural networks on a central server, and fine-tuning on clients to enhance model accuracy and adaptability. A selective hash code sharing mechanism using a privacy-preserving global memory bank further supports client fine-tuning. This approach addresses challenges in previous research by improving computational efficiency and scalability. Practical applications include Car2X event predictions, where a shared model is collectively trained to recognize traffic patterns, aiding in tasks such as traffic density assessment and accident detection. The research aims to develop a robust framework that combines federated learning, deep transfer hashing and transfer learning for efficient and secure downstream task execution.

Related papers

• Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction with Blockchain [8.99791083863972]
  Federated learning combined with blockchain empowers secure data sharing in autonomous driving applications. The lack of data quality audits raises concerns about multi-party mistrust in trajectory prediction tasks. This paper proposes an asynchronous federated learning data sharing method based on an interpretable reputation quantization mechanism.
  arXiv  Detail & Related papers  (2024-07-28T08:34:27Z)
• Blockchain-enabled Trustworthy Federated Unlearning [50.01101423318312]
  Federated unlearning is a promising paradigm for protecting the data ownership of distributed clients. Existing works require central servers to retain the historical model parameters from distributed clients. This paper proposes a new blockchain-enabled trustworthy federated unlearning framework.
  arXiv  Detail & Related papers  (2024-01-29T07:04:48Z)
• Efficient Cross-Domain Federated Learning by MixStyle Approximation [0.3277163122167433]
  We introduce a privacy-preserving, resource-efficient Federated Learning concept for client adaptation in hardware-constrained environments. Our approach includes server model pre-training on source data and subsequent fine-tuning on target data via low-end clients. Preliminary results indicate that our method reduces computational and transmission costs while maintaining competitive performance on downstream tasks.
  arXiv  Detail & Related papers  (2023-12-12T08:33:34Z)
• FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data [54.81695390763957]
  Federated learning is an emerging distributed machine learning method. We propose a heterogeneous local variant of AMSGrad, named FedLALR, in which each client adjusts its learning rate. We show that our client-specified auto-tuned learning rate scheduling can converge and achieve linear speedup with respect to the number of clients.
  arXiv  Detail & Related papers  (2023-09-18T12:35:05Z)
• Effectively Heterogeneous Federated Learning: A Pairing and Split Learning Based Approach [16.093068118849246]
  This paper presents a novel split federated learning (SFL) framework that pairs clients with different computational resources. A greedy algorithm is proposed by reconstructing the optimization of training latency as a graph edge selection problem. Simulation results show the proposed method can significantly improve the FL training speed and achieve high performance.
  arXiv  Detail & Related papers  (2023-08-26T11:10:54Z)
• Optimal transfer protocol by incremental layer defrosting [66.76153955485584]
  Transfer learning is a powerful tool enabling model training with limited amounts of data. The simplest transfer learning protocol is based on freezing" the feature-extractor layers of a network pre-trained on a data-rich source task. We show that this protocol is often sub-optimal and the largest performance gain may be achieved when smaller portions of the pre-trained network are kept frozen.
  arXiv  Detail & Related papers  (2023-03-02T17:32:11Z)
• 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)
• Privacy-Preserving Serverless Edge Learning with Decentralized Small Data [13.254530176359182]
  Distributed training strategies have recently become a promising approach to ensure data privacy when training deep models. This paper extends conventional serverless platforms with serverless edge learning architectures and provides an efficient distributed training framework from the networking perspective.
  arXiv  Detail & Related papers  (2021-11-29T21:04:49Z)
• Comfetch: Federated Learning of Large Networks on Constrained Clients via Sketching [28.990067638230254]
  Federated learning (FL) is a popular paradigm for private and collaborative model training on the edge. We propose a novel algorithm, Comdirectional, which allows clients to train large networks using representations of the global neural network.
  arXiv  Detail & Related papers  (2021-09-17T04:48:42Z)
• Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach [61.64006416975458]
  We propose a privacy-preserving machine learning technique named Federated Learning-based Gated Recurrent Unit neural network algorithm (FedGRU) for traffic flow prediction. FedGRU differs from current centralized learning methods and updates universal learning models through a secure parameter aggregation mechanism. It is shown that FedGRU's prediction accuracy is 90.96% higher than the advanced deep learning models.
  arXiv  Detail & Related papers  (2020-03-19T13:07:49Z)
• Large-Scale Gradient-Free Deep Learning with Recursive Local Representation Alignment [84.57874289554839]
  Training deep neural networks on large-scale datasets requires significant hardware resources. Backpropagation, the workhorse for training these networks, is an inherently sequential process that is difficult to parallelize. We propose a neuro-biologically-plausible alternative to backprop that can be used to train deep networks.
  arXiv  Detail & Related papers  (2020-02-10T16:20:02Z)

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.