Privatized graph federated learning

Privatized graph federated learning

Abstract Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-master multi-client structure. It ca...

Full description

Bibliographic Details
Main Authors:	Elsa Rizk, Stefan Vlaski, Ali H. Sayed
Format:	Article
Language:	English
Published:	SpringerOpen 2023-08-01
Series:	EURASIP Journal on Advances in Signal Processing
Subjects:	Federated learning Distributed learning Privatized learning Differntial privacy
Online Access:	https://doi.org/10.1186/s13634-023-01049-4

Similar Items

Privacy and Security in Federated Learning: A Survey
by: Rémi Gosselin, et al.
Published: (2022-10-01)

SC-FGCL: Self-Adaptive Cluster-Based Federal Graph Contrastive Learning
by: Tingqi Wang, et al.
Published: (2023-01-01)

Evaluating the Impact of Mobility on Differentially Private Federated Learning
by: Eun-ji Kim, et al.
Published: (2024-06-01)

Benchmarking federated strategies in Peer-to-Peer Federated learning for biomedical data
by: Jose L. Salmeron, et al.
Published: (2023-06-01)

Concentrated Differentially Private Federated Learning With Performance Analysis
by: Rui Hu, et al.
Published: (2021-01-01)

LF3PFL: A Practical Privacy-Preserving Federated Learning Algorithm Based on Local Federalization Scheme
by: Yong Li, et al.
Published: (2024-04-01)

LAFD: Local-Differentially Private and Asynchronous Federated Learning With Direct Feedback Alignment
by: Kijung Jung, et al.
Published: (2023-01-01)

Privacy-preserving federated learning for transportation mode prediction based on personal mobility data
by: Fuxun Yu, et al.
Published: (2022-12-01)

Decentralized Federated Learning Over Slotted ALOHA Wireless Mesh Networking
by: Abdelaziz Salama, et al.
Published: (2023-01-01)

Information-theoretic privacy in federated submodel learning
by: Minchul Kim, et al.
Published: (2023-06-01)

A Survey on the use of Federated Learning in Privacy-Preserving Recommender Systems
by: Christos Chronis, et al.
Published: (2024-01-01)

Byzantine-Resilient Secure Federated Learning on Low-Bandwidth Networks
by: Hiroki Masuda, et al.
Published: (2023-01-01)

Hierarchical federated learning with global differential privacy
by: Youqun Long, et al.
Published: (2023-04-01)

Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data
by: Joceline Ziegler, et al.
Published: (2022-07-01)

Research on federated learning approach based on local differential privacy
by: Haiyan KANG, et al.
Published: (2022-10-01)

Research on federated learning approach based on local differential privacy
by: Haiyan KANG, et al.
Published: (2022-10-01)

Federated meta learning: a review
by: Chuanyao ZHANG, et al.
Published: (2023-03-01)

Distributed Synthetic Time-Series Data Generation With Local Differentially Private Federated Learning
by: Xue Jiang, et al.
Published: (2024-01-01)

Federated learning with differential privacy via fast Fourier transform for tighter-efficient combining
by: Shengnan Guo, et al.
Published: (2024-11-01)

FEDEMB: A VERTICAL AND HYBRID FEDERATED LEARNING ALGORITHM USING NETWORK AND FEATURE EMBEDDING AGGREGATION
by: Fanfei Meng, et al.
Published: (2024-06-01)

Accelerating Federated Learning via Sequential Training of Grouped Heterogeneous Clients
by: Andrea Silvi, et al.
Published: (2024-01-01)

Communication-Efficient Vertical Federated Learning
by: Afsana Khan, et al.
Published: (2022-08-01)

A review of federated learning in renewable energy applications: Potential, challenges, and future directions
by: Albin Grataloup, et al.
Published: (2024-09-01)

Research advances on privacy protection of federated learning
by: Jianzong WANG, et al.
Published: (2021-05-01)

DAGUARD: distributed backdoor attack defense scheme under federated learning
by: Shengxing YU, et al.
Published: (2023-05-01)

DAGUARD: distributed backdoor attack defense scheme under federated learning
by: Shengxing YU, et al.
Published: (2023-05-01)

Secure Data Sharing in Federated Learning through Blockchain-Based Aggregation
by: Bowen Liu, et al.
Published: (2024-04-01)

The privacy-explainability trade-off: unraveling the impacts of differential privacy and federated learning on attribution methods
by: Saifullah Saifullah, et al.
Published: (2024-07-01)

Accelerating Fair Federated Learning: Adaptive Federated Adam
by: Li Ju, et al.
Published: (2024-01-01)

A Novel Approach to Machine Learning Application to Protection Privacy Data in Healthcare: Federated Learning
by: Ahmet Ali SÜZEN, et al.
Published: (2020-04-01)

Deep Federated Learning-Based Privacy-Preserving Wind Power Forecasting
by: Amirhossein Ahmadi, et al.
Published: (2023-01-01)

Federated Learning to Safeguard Patients Data: A Medical Image Retrieval Case
by: Gurtaj Singh, et al.
Published: (2023-01-01)

Comparative Analysis of Membership Inference Attacks in Federated and Centralized Learning
by: Ali Abbasi Tadi, et al.
Published: (2023-11-01)

Differential Private Federated Learning in Geographically Distributed Public Administration Processes
by: Mirwais Ahmadzai, et al.
Published: (2024-06-01)

Federated Learning for Short-Term Residential Load Forecasting
by: Christopher Briggs, et al.
Published: (2022-01-01)

Threats and defenses of federated learning: a survey
by: Jianhan WU, et al.
Published: (2022-09-01)

Federated Learning for 6G Networks: Navigating Privacy Benefits and Challenges
by: Chamara Sandeepa, et al.
Published: (2025-01-01)

Federated Learning for privacy-Friendly Health Apps: A Case Study on Ovulation Tracking
by: Nikolaos Pavlidis, et al.
Published: (2025-01-01)

A survey on federated learning in crowd intelligence
by: Qiang YANG, et al.
Published: (2022-03-01)

Blockchain-Based Swarm Learning for the Mitigation of Gradient Leakage in Federated Learning
by: Hussain Ahmad Madni, et al.
Published: (2023-01-01)