Approximate Gradient Coding for Privacy-Flexible Federated Learning with Non-IID Data

• URL: http://arxiv.org/abs/2404.03524v1
• Date: Thu, 4 Apr 2024 15:29:50 GMT
• Title: Approximate Gradient Coding for Privacy-Flexible Federated Learning with Non-IID Data
• Authors: Okko Makkonen, Sampo Niemelä, Camilla Hollanti, Serge Kas Hanna,
• Abstract summary: This work focuses on the challenges of non-IID data and stragglers/dropouts in federated learning. We introduce and explore a privacy-flexible paradigm that models parts of the clients' local data as non-private.
• Score: 9.984630251008868
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work focuses on the challenges of non-IID data and stragglers/dropouts in federated learning. We introduce and explore a privacy-flexible paradigm that models parts of the clients' local data as non-private, offering a more versatile and business-oriented perspective on privacy. Within this framework, we propose a data-driven strategy for mitigating the effects of label heterogeneity and client straggling on federated learning. Our solution combines both offline data sharing and approximate gradient coding techniques. Through numerical simulations using the MNIST dataset, we demonstrate that our approach enables achieving a deliberate trade-off between privacy and utility, leading to improved model convergence and accuracy while using an adaptable portion of non-private data.

Related papers

• Personalized Federated Learning for Cross-view Geo-localization [49.40531019551957]
  We propose a methodology combining Federated Learning (FL) with Cross-view Image Geo-localization (CVGL) techniques. Our method implements a coarse-to-fine approach, where clients share only the coarse feature extractors while keeping fine-grained features specific to local environments. Results demonstrate that our federated CVGL method achieves performance close to centralized training while maintaining data privacy.
  arXiv  Detail & Related papers  (2024-11-07T13:25:52Z)
• Privately Learning from Graphs with Applications in Fine-tuning Large Language Models [16.972086279204174]
  relational data in sensitive domains such as finance and healthcare often contain private information. Existing privacy-preserving methods, such as DP-SGD, are not well-suited for relational learning. We propose a privacy-preserving relational learning pipeline that decouples dependencies in sampled relations during training.
  arXiv  Detail & Related papers  (2024-10-10T18:38:38Z)
• Differentially Private Active Learning: Balancing Effective Data Selection and Privacy [11.716423801223776]
  We introduce differentially private active learning (DP-AL) for standard learning settings. We demonstrate that naively integrating DP-SGD training into AL presents substantial challenges in privacy budget allocation and data utilization. Our experiments on vision and natural language processing tasks show that DP-AL can improve performance for specific datasets and model architectures.
  arXiv  Detail & Related papers  (2024-10-01T09:34:06Z)
• FewFedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning [54.26614091429253]
  Federated instruction tuning (FedIT) is a promising solution, by consolidating collaborative training across multiple data owners. FedIT encounters limitations such as scarcity of instructional data and risk of exposure to training data extraction attacks. We propose FewFedPIT, designed to simultaneously enhance privacy protection and model performance of federated few-shot learning.
  arXiv  Detail & Related papers  (2024-03-10T08:41:22Z)
• A chaotic maps-based privacy-preserving distributed deep learning for incomplete and Non-IID datasets [1.30536490219656]
  Federated Learning is a machine learning approach that enables the training of a deep learning model among several participants with sensitive data. In this research, the authors employ a secured Federated Learning method with an additional layer of privacy and propose a method for addressing the non-IID challenge.
  arXiv  Detail & Related papers  (2024-02-15T17:49:50Z)
• Personalized Federated Learning with Attention-based Client Selection [57.71009302168411]
  We propose FedACS, a new PFL algorithm with an Attention-based Client Selection mechanism. FedACS integrates an attention mechanism to enhance collaboration among clients with similar data distributions. Experiments on CIFAR10 and FMNIST validate FedACS's superiority.
  arXiv  Detail & Related papers  (2023-12-23T03:31:46Z)
• DCFL: Non-IID awareness Data Condensation aided Federated Learning [0.8158530638728501]
  Federated learning is a decentralized learning paradigm wherein a central server trains a global model iteratively by utilizing clients who possess a certain amount of private datasets. The challenge lies in the fact that the client side private data may not be identically and independently distributed. We propose DCFL which divides clients into groups by using the Centered Kernel Alignment (CKA) method, then uses dataset condensation methods with non-IID awareness to complete clients.
  arXiv  Detail & Related papers  (2023-12-21T13:04:24Z)
• Federated Learning Empowered by Generative Content [55.576885852501775]
  Federated learning (FL) enables leveraging distributed private data for model training in a privacy-preserving way. We propose a novel FL framework termed FedGC, designed to mitigate data heterogeneity issues by diversifying private data with generative content. We conduct a systematic empirical study on FedGC, covering diverse baselines, datasets, scenarios, and modalities.
  arXiv  Detail & Related papers  (2023-12-10T07:38:56Z)
• Private Set Generation with Discriminative Information [63.851085173614]
  Differentially private data generation is a promising solution to the data privacy challenge. Existing private generative models are struggling with the utility of synthetic samples. We introduce a simple yet effective method that greatly improves the sample utility of state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-11-07T10:02:55Z)
• Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data [20.757477553095637]
  Federated learning (FL) is a privacy-promoting framework that enables clients to collaboratively train machine learning models. A major challenge in federated learning arises when the local data is heterogeneous. We propose FedDPMS, an FL algorithm in which clients deploy variational auto-encoders to augment local datasets with data synthesized using differentially private means of latent data representations.
  arXiv  Detail & Related papers  (2022-06-01T18:00:48Z)
• Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning [61.488646649045215]
  Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices)
  arXiv  Detail & Related papers  (2021-11-28T19:03:39Z)

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.