UVeQFed: Universal Vector Quantization for Federated Learning

• URL: http://arxiv.org/abs/2006.03262v3
• Date: Mon, 14 Dec 2020 10:51:33 GMT
• Title: UVeQFed: Universal Vector Quantization for Federated Learning
• Authors: Nir Shlezinger, Mingzhe Chen, Yonina C. Eldar, H. Vincent Poor, and Shuguang Cui
• Abstract summary: Federated learning (FL) is an emerging approach to train such learning models without requiring the users to share their possibly private labeled data. In FL, each user trains its copy of the learning model locally. The server then collects the individual updates and aggregates them into a global model. We show that combining universal vector quantization methods with FL yields a decentralized training system in which the compression of the trained models induces only a minimum distortion.
• Score: 179.06583469293386
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional deep learning models are trained at a centralized server using labeled data samples collected from end devices or users. Such data samples often include private information, which the users may not be willing to share. Federated learning (FL) is an emerging approach to train such learning models without requiring the users to share their possibly private labeled data. In FL, each user trains its copy of the learning model locally. The server then collects the individual updates and aggregates them into a global model. A major challenge that arises in this method is the need of each user to efficiently transmit its learned model over the throughput limited uplink channel. In this work, we tackle this challenge using tools from quantization theory. In particular, we identify the unique characteristics associated with conveying trained models over rate-constrained channels, and propose a suitable quantization scheme for such settings, referred to as universal vector quantization for FL (UVeQFed). We show that combining universal vector quantization methods with FL yields a decentralized training system in which the compression of the trained models induces only a minimum distortion. We then theoretically analyze the distortion, showing that it vanishes as the number of users grows. We also characterize the convergence of models trained with the traditional federated averaging method combined with UVeQFed to the model which minimizes the loss function. Our numerical results demonstrate the gains of UVeQFed over previously proposed methods in terms of both distortion induced in quantization and accuracy of the resulting aggregated model.

Related papers

• MultiConfederated Learning: Inclusive Non-IID Data handling with Decentralized Federated Learning [1.2726316791083532]
  Federated Learning (FL) has emerged as a prominent privacy-preserving technique for enabling use cases like confidential clinical machine learning. FL operates by aggregating models trained by remote devices which owns the data. We propose MultiConfederated Learning: a decentralized FL framework which is designed to handle non-IID data.
  arXiv  Detail & Related papers  (2024-04-20T16:38:26Z)
• Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes [54.18186259484828]
  In Federated Learning (FL) paradigm, a parameter server (PS) concurrently communicates with distributed participating clients for model collection, update aggregation, and model distribution over multiple rounds. We show strong evidences that variable-length is beneficial for compression in FL. We present Fed-CVLC (Federated Learning Compression with Variable-Length Codes), which fine-tunes the code length in response to the dynamics of model updates.
  arXiv  Detail & Related papers  (2024-02-06T07:25:21Z)
• Exploiting Label Skews in Federated Learning with Model Concatenation [39.38427550571378]
  Federated Learning (FL) has emerged as a promising solution to perform deep learning on different data owners without exchanging raw data. Among different non-IID types, label skews have been challenging and common in image classification and other tasks. We propose FedConcat, a simple and effective approach that degrades these local models as the base of the global model.
  arXiv  Detail & Related papers  (2023-12-11T10:44:52Z)
• Straggler-resilient Federated Learning: Tackling Computation Heterogeneity with Layer-wise Partial Model Training in Mobile Edge Network [4.1813760301635705]
  We propose Federated Partial Model Training (FedPMT), where devices with smaller computational capabilities work on partial models and contribute to the global model. As such, all devices in FedPMT prioritize the most crucial parts of the global model. Empirical results show that FedPMT significantly outperforms the existing benchmark FedDrop.
  arXiv  Detail & Related papers  (2023-11-16T16:30:04Z)
• Tunable Soft Prompts are Messengers in Federated Learning [55.924749085481544]
  Federated learning (FL) enables multiple participants to collaboratively train machine learning models using decentralized data sources. The lack of model privacy protection in FL becomes an unneglectable challenge. We propose a novel FL training approach that accomplishes information exchange among participants via tunable soft prompts.
  arXiv  Detail & Related papers  (2023-11-12T11:01:10Z)
• 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)
• Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning [86.59588262014456]
  Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. We propose a data-free knowledge distillation method to fine-tune the global model in the server (FedFTG) Our FedFTG significantly outperforms the state-of-the-art (SOTA) FL algorithms and can serve as a strong plugin for enhancing FedAvg, FedProx, FedDyn, and SCAFFOLD.
  arXiv  Detail & Related papers  (2022-03-17T11:18:17Z)
• FedCAT: Towards Accurate Federated Learning via Device Concatenation [4.416919766772866]
  Federated Learning (FL) enables all the involved devices to train a global model collaboratively without exposing their local data privacy. For non-IID scenarios, the classification accuracy of FL models decreases drastically due to the weight divergence caused by data heterogeneity. We introduce a novel FL approach named Fed-Cat that can achieve high model accuracy based on our proposed device selection strategy and device concatenation-based local training method.
  arXiv  Detail & Related papers  (2022-02-23T10:08:43Z)
• 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)
• Over-the-Air Federated Learning from Heterogeneous Data [107.05618009955094]
  Federated learning (FL) is a framework for distributed learning of centralized models. We develop a Convergent OTA FL (COTAF) algorithm which enhances the common local gradient descent (SGD) FL algorithm. We numerically show that the precoding induced by COTAF notably improves the convergence rate and the accuracy of models trained via OTA FL.
  arXiv  Detail & Related papers  (2020-09-27T08:28:25Z)

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.