Cover Image

FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Cheng, Chen, Ziyi, Zhou, Yi, Kailkhura, Bhavya
Format:	Preprint
Published:	2020
Subjects:	Machine Learning Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver
by: Ma, Shaocong, et al.
Published: (2024)

FedGiA: An Efficient Hybrid Algorithm for Federated Learning
by: Zhou, Shenglong, et al.
Published: (2022)

Implicit Bias and Fast Convergence Rates for Self-attention
by: Vasudeva, Bhavya, et al.
Published: (2024)

Trade-off in Estimating the Number of Byzantine Clients in Federated Learning
by: Chen, Ziyi, et al.
Published: (2025)

Revisiting Convergence: Shuffling Complexity Beyond Lipschitz Smoothness
by: He, Qi, et al.
Published: (2025)

FedSEA: Achieving Benefit of Parallelization in Federated Online Learning
by: Sahu, Harekrushna, et al.
Published: (2026)

Provably Convergent Federated Trilevel Learning
by: Jiao, Yang, et al.
Published: (2023)

FedMuon: Federated Learning with Bias-corrected LMO-based Optimization
by: Takezawa, Yuki, et al.
Published: (2025)

Achieve Performatively Optimal Policy for Performative Reinforcement Learning
by: Chen, Ziyi, et al.
Published: (2025)

Statistically Optimal K-means Clustering via Nonnegative Low-rank Semidefinite Programming
by: Zhuang, Yubo, et al.
Published: (2023)

Aiding Global Convergence in Federated Learning via Local Perturbation and Mutual Similarity Information
by: Buttaci, Emanuel, et al.
Published: (2024)

FedSUM Family: Efficient Federated Learning Methods under Arbitrary Client Participation
by: You, Runze, et al.
Published: (2025)

Optimal and Order-optimal Gated Priority-based Greedy Policies for Two-layer Multi-item Order Fulfillment
by: Chen, Xi, et al.
Published: (2026)

Communication Efficient Federated Learning with Linear Convergence on Heterogeneous Data
by: Liu, Jie, et al.
Published: (2025)

Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing
by: Chen, Cheng, et al.
Published: (2021)

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems
by: Goyal, Girnar, et al.
Published: (2025)

Scalable Second-order Riemannian Optimization for $K$-means Clustering
by: Xu, Peng, et al.
Published: (2025)

Gradient Regularized Newton Boosting Trees with Global Convergence
by: Zozoulenko, Nikita, et al.
Published: (2026)

Distributionally Robust Multi-Objective Optimization
by: Yang, Yufeng, et al.
Published: (2026)

Surrogate-based Optimization via Clustering for Box-Constrained Problems
by: Ahmad, Maaz, et al.
Published: (2026)

Masked Subspace Clustering Methods
by: Song, Jiebo, et al.
Published: (2025)

Reusing Historical Trajectories in Natural Policy Gradient via Importance Sampling: Convergence and Convergence Rate
by: Lin, Yifan, et al.
Published: (2024)

Neural Network Training Techniques Regularize Optimization Trajectory: An Empirical Study
by: Chen, Cheng, et al.
Published: (2020)

On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization
by: Zhou, Dongruo, et al.
Published: (2018)

Sobolev Gradient Ascent for Optimal Transport: Barycenter Optimization and Convergence Analysis
by: Kim, Kaheon, et al.
Published: (2025)

Online Cluster-Based Parameter Control for Metaheuristic
by: Tatsis, Vasileios A., et al.
Published: (2025)

On the Convergence of Gradient Descent on Learning Transformers with Residual Connections
by: Qin, Zhen, et al.
Published: (2025)

On Convergence of Average-Reward Q-Learning in Weakly Communicating Markov Decision Processes
by: Wan, Yi, et al.
Published: (2024)

Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance
by: Zhang, Qi, et al.
Published: (2024)

Greedy Low-Rank Gradient Compression for Distributed Learning with Convergence Guarantees
by: Chen, Chuyan, et al.
Published: (2025)

Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries
by: Ganesh, Swetha, et al.
Published: (2024)

FedSGM: A Unified Framework for Constraint Aware, Bidirectionally Compressed, Multi-Step Federated Optimization
by: Upadhyay, Antesh, et al.
Published: (2026)

Convergence Analysis of Stochastic Gradient Descent with MCMC Estimators
by: Li, Tianyou, et al.
Published: (2023)

FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering
by: Islam, Md Sirajul, et al.
Published: (2024)

Zeroth-Order Methods for Stochastic Nonconvex Nonsmooth Composite Optimization
by: Chen, Ziyi, et al.
Published: (2025)

Convergence of Distributed Adaptive Optimization with Local Updates
by: Cheng, Ziheng, et al.
Published: (2024)

Strong bounds for large-scale Minimum Sum-of-Squares Clustering
by: Croella, Anna Livia, et al.
Published: (2025)

Convergence Analysis of the Wasserstein Proximal Algorithm beyond Geodesic Convexity
by: Zhu, Shuailong, et al.
Published: (2025)

Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approach
by: Fernando, Heshan, et al.
Published: (2022)

FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering
by: Islam, Md Sirajul, et al.
Published: (2024)