mlsanityData to 14 Oct 2026

Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning

arXiv 2505.01788 ↗First posted 3 May 2025cs.LG cs.CR cs.DCcited by 5

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 31 of its 46 references that are in the map. First posted on arXiv 3 May 2025.

Builds on

31 of 46 references.

References · 31

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2016Deep Learning with Differential Privacycited by 2,728
  5. 2021Model-Contrastive Federated Learningcited by 766
  6. 2017Federated Multi-Task Learningcited by 672
  7. 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 672
  8. 2020Personalized Federated Learning with Moreau Envelopescited by 618
  9. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 571
  10. 2021Federated Learning Based on Dynamic Regularizationcited by 562
  11. 2019Federated Learning with Personalization Layerscited by 541
  12. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 541
19 more
  1. 2019Federated Machine Learning: Concept and Applicationscited by 412
  2. 2018Tune: A Research Platform for Distributed Model Selection and Trainingcited by 395
  3. 2020Adaptive Personalized Federated Learningcited by 358
  4. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 352
  5. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 325
  6. 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 283
  7. 2020Personalized Federated Learning with First Order Model Optimizationcited by 201
  8. 2021FedKD: Communication Efficient Federated Learning via Knowledge Distillationcited by 187
  9. 2021FedBABU: Towards Enhanced Representation for Federated Image Classificationcited by 182
  10. 2016Practical Secure Aggregation for Federated Learning on User-Held Datacited by 178
  11. 2022Federated Learning from Pre-Trained Models: A Contrastive Learning Approachcited by 142
  12. 2022FedALA: Adaptive Local Aggregation for Personalized Federated Learningcited by 122
  13. 2023Personalized Federated Learning with Feature Alignment and Classifier Collaborationcited by 97
  14. 2020Federated Mutual Learningcited by 71
  15. 2021Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learningcited by 53
  16. 2020Stochastic-Sign SGD for Federated Learning with Theoretical Guaranteescited by 35
  17. 2021Federated Learning for Face Recognition with Gradient Correctioncited by 24
  18. 2020A Comparison of Optimization Algorithms for Deep Learningcited by 23
  19. 2019Insecure Until Proven Updated: Analyzing AMD SEV's Remote Attestationcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025Advancing Exchange Rate Forecasting: Leveraging Machine Learning and AI for Enhanced Accuracy in Global Financial Marketscited by 2
  2. 2025ViMoNet: A Multimodal Vision-Language Framework for Human Behavior Understanding from Motion and Videocited by 0
  3. 2025GANDiff FR: Hybrid GAN Diffusion Synthesis for Causal Bias Attribution in Face Recognitioncited by 0
  4. 2026Cross-Lingual Probing and Community-Grounded Analysis of Gender Bias in Low-Resource Bengalicited by 0
  5. 2026Pattern Recognition Tasks with Personalized Federated Learningcited by 0