Communication-efficient Federated Learning through Clustering optimization (original) (raw)

Edge-Based Communication Optimization for Distributed Federated Learning

Hong-Ning Dai

IEEE Transactions on Network Science and Engineering, 2022

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Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study

Ali Payani

arXiv (Cornell University), 2024

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Optimized Federated Learning on Class-Biased Distributed Data Sources

Chunming Rong

Communications in Computer and Information Science, 2021

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Fast-Convergent Federated Learning With Adaptive Weighting

Hongda Wu

IEEE Transactions on Cognitive Communications and Networking, 2021

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Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems

Omar Abdel Wahab

IEEE Communications Surveys & Tutorials, 2021

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Exploiting Features and Logits in Heterogeneous Federated Learning

Yun Hin Chan

arXiv (Cornell University), 2022

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Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networks

Ala Al-Fuqaha

2021 IEEE Global Communications Conference (GLOBECOM), 2021

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Migrating Models: A Decentralized View on Federated Learning

Péter Kiss

Communications in Computer and Information Science, 2021

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Advancements in Privacy-Preserving Techniques for Federated Learning: A Machine Learning Perspective

Monika Rokade

Deleted Journal, 2024

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Analysis of the Factors Influencing the Predictive Learning Performance Using Federated Learning

Shahid Naseem

Research Square (Research Square), 2023

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Making a secure framework for Federated Learning

Vaibhav gusain

2021

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A Survey on Challenges of Federated Learning

Samir Aliyev

Azerbaijan Journal of High Performance Computing

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Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning

Yun Hin Chan

arXiv (Cornell University), 2023

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CONTRA: Defending Against Poisoning Attacks in Federated Learning

Sana Awan

Computer Security – ESORICS 2021, 2021

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Challenges, Applications and Design Aspects of Federated Learning: A Survey

K M Jawadur Rahman

IEEE Access

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Federated Learning: Challenges, Methods, and Future Directions

Mr. Murari Kumar Singh (SET Assistant Professor)

IEEE Signal Processing Magazine, 2020

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Applications of Federated Learning; Taxonomy, Challenges, and Research Trends

Dr. Tariq Umer

Electronics, 2022

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Edge-Assisted Democratized Learning Toward Federated Analytics

Kyi Thar

IEEE Internet of Things Journal, 2022

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A strategy to the reduction of communication overhead and overfitting in Federated Learning

Alex barros

Anais do XXVI Workshop de Gerência e Operação de Redes e Serviços (WGRS 2021), 2021

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MOHAWK: Mobility and Heterogeneity-Aware Dynamic Community Selection for Hierarchical Federated Learning

Radu Marculescu

Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation

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Optimization in Federated Learning

Péter Kiss

2019

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Federated Learning: Opportunities and Challenges

Priyanka Mary Mammen

ArXiv, 2021

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Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition

Faisal Hamman

arXiv (Cornell University), 2023

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FedAVO: Improving Communication Efficiency in Federated Learning with African Vultures Optimizer

Ahmed Imteaj

arXiv (Cornell University), 2023

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Flex: Flexible Federated Learning Framework

Cristina Zuheros

2024

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A Comprehensive Survey on Federated Learning: Concept and Applications

Mohammed Hamzah Abed

2022

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FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning

Ahmed Khaled

Cornell University - arXiv, 2021

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A Framework for Evaluating Gradient Leakage Attacks in Federated Learning

Margaret Loper

arXiv (Cornell University), 2020

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IBM Federated Learning: an Enterprise Framework White Paper V0.1

Annie Abay

ArXiv, 2020

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Federated Learning Design and FunctionalModels: Survey

John Ayeelyan

Research Square (Research Square), 2022

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Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications

Mohammad ali vahedifar

arXiv (Cornell University), 2023

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Towards Mobile Federated Learning with Unreliable Participants and Selective Aggregation

Gabriel Falcao

Applied sciences, 2023

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Resource Aware Clustering for Tackling the Heterogeneity of Participants in Federated Learning

Garvit Banga

arXiv (Cornell University), 2023

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K-FL: Kalman Filter-Based Clustering Federated Learning Method

Hyungbin Kim

IEEE Access

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Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning

Faissal El Bouanani

IEEE Internet of Things Journal

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