Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning

• URL: http://arxiv.org/abs/2505.01788v1
• Date: Sat, 03 May 2025 11:31:38 GMT
• Title: Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning
• Authors: Md. Tanzib Hosain, Asif Zaman, Md. Shahriar Sajid, Shadman Sakeeb Khan, Shanjida Akter,
• Abstract summary: There has been a seismic shift in interest towards the leading paradigm for training Machine Learning (ML) models on decentralized data silos while maintaining data privacy, Federated Learning (FL)<n>This research paper presents a comprehensive performance analysis of a cutting-edge approach to personalize ML model while preserving privacy achieved through Privacy Preserving Machine Learning.<n>According to our analysis, Adaptive Personalized Cross-Silo Federated Learning with Differential Privacy (APPLE+DP) offering efficient execution whereas overall, the use of the Adaptive Personalized Cross-Silo Federated Learning with Homomorphic Encryption (APPLE+HE) algorithm for privacy-preserving machine learning tasks is strongly
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The widespread adoption of Artificial Intelligence (AI) has been driven by significant advances in intelligent system research. However, this progress has raised concerns about data privacy, leading to a growing awareness of the need for privacy-preserving AI. In response, there has been a seismic shift in interest towards the leading paradigm for training Machine Learning (ML) models on decentralized data silos while maintaining data privacy, Federated Learning (FL). This research paper presents a comprehensive performance analysis of a cutting-edge approach to personalize ML model while preserving privacy achieved through Privacy Preserving Machine Learning with the innovative framework of Federated Personalized Learning (PPMLFPL). Regarding the increasing concerns about data privacy, this study evaluates the effectiveness of PPMLFPL addressing the critical balance between personalized model refinement and maintaining the confidentiality of individual user data. According to our analysis, Adaptive Personalized Cross-Silo Federated Learning with Differential Privacy (APPLE+DP) offering efficient execution whereas overall, the use of the Adaptive Personalized Cross-Silo Federated Learning with Homomorphic Encryption (APPLE+HE) algorithm for privacy-preserving machine learning tasks in federated personalized learning settings is strongly suggested. The results offer valuable insights creating it a promising scope for future advancements in the field of privacy-conscious data-driven technologies.

Related papers

• Differential Privacy in Machine Learning: From Symbolic AI to LLMs [49.1574468325115]
  Differential privacy provides a formal framework to mitigate privacy risks.<n>It ensures that the inclusion or exclusion of any single data point does not significantly alter the output of an algorithm.
  arXiv  Detail & Related papers  (2025-06-13T11:30:35Z)
• Towards Artificial General or Personalized Intelligence? A Survey on Foundation Models for Personalized Federated Intelligence [59.498447610998525]
  The rise of large language models (LLMs) has reshaped the artificial intelligence landscape.<n>This paper focuses on adapting these powerful models to meet the specific needs and preferences of users while maintaining privacy and efficiency.<n>We propose personalized federated intelligence (PFI), which integrates the privacy-preserving advantages of federated learning with the zero-shot generalization capabilities of FMs.
  arXiv  Detail & Related papers  (2025-05-11T08:57:53Z)
• Adaptive Clipping for Privacy-Preserving Few-Shot Learning: Enhancing Generalization with Limited Data [12.614480013684759]
  We introduce a novel approach called Meta-Clip to enhance the utility of privacy-preserving few-shot learning methods.<n>By dynamically adjusting clipping thresholds during the training process, our Adaptive Clipping method provides fine-grained control over the disclosure of sensitive information.<n>We demonstrate the effectiveness of our approach in minimizing utility degradation, showcasing a superior privacy-preserving trade-off compared to existing privacy-preserving techniques.
  arXiv  Detail & Related papers  (2025-03-27T05:14:18Z)
• Advancing Personalized Federated Learning: Integrative Approaches with AI for Enhanced Privacy and Customization [0.0]
  This paper proposes a novel approach that enhances PFL with cutting-edge AI techniques.<n>We present a model that boosts the performance of individual client models and ensures robust privacy-preserving mechanisms.<n>This work paves the way for a new era of truly personalized and privacy-conscious AI systems.
  arXiv  Detail & Related papers  (2025-01-30T07:03:29Z)
• Privacy Drift: Evolving Privacy Concerns in Incremental Learning [4.275908952997288]
  This study aims to unveil the nuanced relationship between the evolution of model performance and the integrity of data privacy.<n>Our results highlight a complex interplay between model accuracy and privacy safeguards, revealing that enhancements in model performance can lead to increased privacy risks.<n>This work lays the groundwork for future research on privacy-aware machine learning, aiming to achieve a delicate balance between model accuracy and data privacy in decentralized environments.
  arXiv  Detail & Related papers  (2024-12-06T17:04:09Z)
• Activity Recognition on Avatar-Anonymized Datasets with Masked Differential Privacy [64.32494202656801]
  Privacy-preserving computer vision is an important emerging problem in machine learning and artificial intelligence.<n>We present anonymization pipeline that replaces sensitive human subjects in video datasets with synthetic avatars within context.<n>We also proposeMaskDP to protect non-anonymized but privacy sensitive background information.
  arXiv  Detail & Related papers  (2024-10-22T15:22:53Z)
• 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)
• PrivacyMind: Large Language Models Can Be Contextual Privacy Protection Learners [81.571305826793]
  We introduce Contextual Privacy Protection Language Models (PrivacyMind) Our work offers a theoretical analysis for model design and benchmarks various techniques. In particular, instruction tuning with both positive and negative examples stands out as a promising method.
  arXiv  Detail & Related papers  (2023-10-03T22:37:01Z)
• A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
  Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
  arXiv  Detail & Related papers  (2023-09-27T14:38:16Z)
• "You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated Learning [78.70083858195906]
  We propose differentially private mechanisms to measure differences in performance across groups while protecting the privacy of group membership. Our results show that, contrary to what prior work suggested, protecting privacy is not necessarily in conflict with identifying performance disparities of federated models.
  arXiv  Detail & Related papers  (2022-06-24T09:46:43Z)
• Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization [57.98426940386627]
  We show that coordinating local learning with private centralized learning yields a generically useful and improved tradeoff between accuracy and privacy. We illustrate our theoretical results with experiments on synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2022-02-10T20:44:44Z)
• Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy [8.30788601976591]
  We present the Sherpa.ai Federated Learning framework that is built upon an holistic view of federated learning and differential privacy. We show how to follow the methodological guidelines with the Sherpa.ai Federated Learning framework by means of a classification and a regression use cases.
  arXiv  Detail & Related papers  (2020-07-02T06:47:35Z)
• Anonymizing Data for Privacy-Preserving Federated Learning [3.3673553810697827]
  We propose the first syntactic approach for offering privacy in the context of federated learning. Our approach aims to maximize utility or model performance, while supporting a defensible level of privacy. We perform a comprehensive empirical evaluation on two important problems in the healthcare domain, using real-world electronic health data of 1 million patients.
  arXiv  Detail & Related papers  (2020-02-21T02:30:16Z)

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.