Cover Image

Fine-Tuning Foundation Models with Federated Learning for Privacy Preserving Medical Time Series Forecasting

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ali, Mahad, Lisle, Curtis, Moore, Patrick W., Barkouki, Tammer, Kirkwood, Brian J., Brattain, Laura J.
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models
by: Yu, Sixing, et al.
Published: (2023)

PrivTuner with Homomorphic Encryption and LoRA: A P3EFT Scheme for Privacy-Preserving Parameter-Efficient Fine-Tuning of AI Foundation Models
by: Li, Yang, et al.
Published: (2024)

Federated Learning-Enhanced Blockchain Framework for Privacy-Preserving Intrusion Detection in Industrial IoT
by: Ali, Anas, et al.
Published: (2025)

Privacy-Preserving Parameter-Efficient Fine-Tuning for Large Language Model Services
by: Li, Yansong, et al.
Published: (2023)

FMCL: Class-Aware Client Clustering with Foundation Model Representations for Heterogeneous Federated Learning
by: Ali, Mahad, et al.
Published: (2026)

Privacy-Preserving Federated Unlearning with Certified Client Removal
by: Liu, Ziyao, et al.
Published: (2024)

Enabling Privacy-Preserving Cyber Threat Detection with Federated Learning
by: Bi, Yu, et al.
Published: (2024)

Efficient Byzantine-Robust and Provably Privacy-Preserving Federated Learning
by: Nie, Chenfei, et al.
Published: (2024)

A Privacy-Preserving Information-Sharing Protocol for Federated Authentication
by: Buccafurri, Francesco, et al.
Published: (2025)

Efficient Secure Aggregation for Privacy-Preserving Federated Machine Learning
by: Behnia, Rouzbeh, et al.
Published: (2023)

PrivFED -- A Framework for Privacy-Preserving Federated Learning in Enhanced Breast Cancer Diagnosis
by: Jha, Maithili, et al.
Published: (2024)

Accelerating Privacy-Preserving Medical Record Linkage: A Three-Party MPC Approach
by: Mağara, Şeyma Selcan, et al.
Published: (2024)

Protecting Privacy in Federated Time Series Analysis: A Pragmatic Technology Review for Application Developers
by: Bachlechner, Daniel, et al.
Published: (2024)

Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks
by: Scheliga, Daniel, et al.
Published: (2023)

Bridging Local and Federated Data Normalization in Federated Learning: A Privacy-Preserving Approach
by: Coşğun, Melih, et al.
Published: (2025)

Preserving Privacy and Security in Federated Learning
by: Nguyen, Truc, et al.
Published: (2022)

Evaluating the Impact of Privacy-Preserving Federated Learning on CAN Intrusion Detection
by: Digregorio, Gabriele, et al.
Published: (2025)

Towards Privacy-Preserving Federated Learning using Hybrid Homomorphic Encryption
by: Costa, Ivan, et al.
Published: (2026)

Efficient and Privacy-Preserving Federated Learning based on Full Homomorphic Encryption
by: Guo, Yuqi, et al.
Published: (2024)

Privacy-Preserving Federated Learning Framework for Risk-Based Adaptive Authentication
by: Baseri, Yaser, et al.
Published: (2025)

Privacy Preserving Data Imputation via Multi-party Computation for Medical Applications
by: Jentsch, Julia, et al.
Published: (2024)

Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting
by: Schuchardt, Jan, et al.
Published: (2025)

Enhancing Security Using Random Binary Weights in Privacy-Preserving Federated Learning
by: Sawada, Hiroto, et al.
Published: (2024)

FedCAPrivacy: Privacy-Preserving Heterogeneous Federated Learning with Anonymous Adaptive Clustering
by: Wei, Yunan, et al.
Published: (2025)

Efficient Byzantine-Robust Privacy-Preserving Federated Learning via Dimension Compression
by: Qin, Xian, et al.
Published: (2025)

Secure Multi-Key Homomorphic Encryption with Application to Privacy-Preserving Federated Learning
by: Wu, Jiahui, et al.
Published: (2025)

FLRSP: Privacy-Preserving Federated Learning Using Randomly Selected Model Parameters
by: Sawada, Hiroto, et al.
Published: (2026)

RewardDS: Privacy-Preserving Fine-Tuning for Large Language Models via Reward Driven Data Synthesis
by: Wang, Jianwei, et al.
Published: (2025)

Knowledge-Assisted Privacy Preserving in Semantic Communication
by: Liu, Xuesong, et al.
Published: (2024)

Towards Privacy-Preserving Revocation of Verifiable Credentials with Time-Flexibility
by: Buccafurri, Francesco, et al.
Published: (2025)

Blockchain-Integrated Privacy-Preserving Medical Insurance Claim Processing Using Homomorphic Encryption
by: Mamoria, Diya, et al.
Published: (2025)

Towards Privacy-Preserving Medical Imaging: Federated Learning with Differential Privacy and Secure Aggregation Using a Modified ResNet Architecture
by: Fares, Mohamad Haj, et al.
Published: (2024)

DP-FedLoRA: Privacy-Enhanced Federated Fine-Tuning for On-Device Large Language Models
by: Xu, Honghui, et al.
Published: (2025)

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption
by: Omar, Abdullah Al, et al.
Published: (2025)

Privacy-Preserving Federated Learning Scheme with Mitigating Model Poisoning Attacks: Vulnerabilities and Countermeasures
by: Wu, Jiahui, et al.
Published: (2025)

Privacy-Preserving Intrusion Detection in Software-defined VANET using Federated Learning with BERT
by: Ahsan, Shakil Ibne, et al.
Published: (2024)

Towards Securing IIoT: An Innovative Privacy-Preserving Anomaly Detector Based on Federated Learning
by: Poorazad, Samira Kamali, et al.
Published: (2026)

PrivTune: Efficient and Privacy-Preserving Fine-Tuning of Large Language Models via Device-Cloud Collaboration
by: Liu, Yi, et al.
Published: (2025)

Efficient Federated Unlearning with Adaptive Differential Privacy Preservation
by: Jiang, Yu, et al.
Published: (2024)

Privacy-Preserving Decentralized Federated Learning via Explainable Adaptive Differential Privacy
by: Piran, Fardin Jalil, et al.
Published: (2025)