Copertina

Federated Learning by Utility-Constrained Stochastic Aggregation for Improving Rational Participation

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Yashwanth, M, Singh, Arunabh, Nayak, Ashok, Bulusu, Sai Kiran, Chakraborty, Anirban
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Minimizing Layerwise Activation Norm Improves Generalization in Federated Learning
di: Yashwanth, M, et al.
Pubblicazione: (2025)

Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning
di: Yashwanth, M, et al.
Pubblicazione: (2023)

Prompt Estimation from Prototypes for Federated Prompt Tuning of Vision Transformers
di: Yashwanth, M, et al.
Pubblicazione: (2025)

FedSCAl: Leveraging Server and Client Alignment for Unsupervised Federated Source-Free Domain Adaptation
di: Yashwanth, M, et al.
Pubblicazione: (2025)

On the Structure of Floating-Point Noise in Batch-Invariant GPU Matrix Multiplication
di: Yashwanth, Tadisetty Sai
Pubblicazione: (2025)

LeARN: Learnable and Adaptive Representations for Nonlinear Dynamics in System Identification
di: Singh, Arunabh, et al.
Pubblicazione: (2024)

Mitigating Participation Imbalance Bias in Asynchronous Federated Learning
di: Chang, Xiangyu, et al.
Pubblicazione: (2025)

Constrained Reweighting of Distributions: an Optimal Transport Approach
di: Chakraborty, Abhisek, et al.
Pubblicazione: (2023)

Cumulative Utility Parity for Fair Federated Learning under Intermittent Client Participation
di: Behfar, Stefan, et al.
Pubblicazione: (2026)

Bayesian Robust Aggregation for Federated Learning
di: Karakulev, Aleksandr, et al.
Pubblicazione: (2025)

Learning non-Gaussian spatial distributions via Bayesian transport maps with parametric shrinkage
di: Chakraborty, Anirban, et al.
Pubblicazione: (2024)

Optimizing Federated Learning for Scalable Power-demand Forecasting in Microgrids
di: Banerjee, Roopkatha, et al.
Pubblicazione: (2025)

Delayed Momentum Aggregation: Communication-efficient Byzantine-robust Federated Learning with Partial Participation
di: Otsuka, Kaoru, et al.
Pubblicazione: (2025)

Private Sketches for Linear Regression
di: Das, Shrutimoy, et al.
Pubblicazione: (2025)

A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning
di: Gu, Hanlin, et al.
Pubblicazione: (2023)

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer
di: Xu, Longtao, et al.
Pubblicazione: (2026)

On the Global Optimality of Policy Gradient Methods in General Utility Reinforcement Learning
di: Barakat, Anas, et al.
Pubblicazione: (2024)

FedShift: Robust Federated Learning Aggregation Scheme in Resource Constrained Environment via Weight Shifting
di: Seo, Jungwon, et al.
Pubblicazione: (2024)

Data-free Defense of Black Box Models Against Adversarial Attacks
di: Nayak, Gaurav Kumar, et al.
Pubblicazione: (2022)

ASMR: Angular Support for Malfunctioning Client Resilience in Federated Learning
di: Konstantin, Mirko, et al.
Pubblicazione: (2025)

Semantic-Constrained Federated Aggregation: Convergence Theory and Privacy-Utility Bounds for Knowledge-Enhanced Distributed Learning
di: Arafat, Jahidul
Pubblicazione: (2025)

Improving Stochastic Action-Constrained Reinforcement Learning via Truncated Distributions
di: Stolz, Roland, et al.
Pubblicazione: (2025)

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning
di: Surdej, Rafał, et al.
Pubblicazione: (2025)

Controlling Participation in Federated Learning with Feedback
di: Cummins, Michael, et al.
Pubblicazione: (2024)

RewardRank: Optimizing True Learning-to-Rank Utility
di: Bhatt, Gaurav, et al.
Pubblicazione: (2025)

Byzantine-Robust Federated Learning with Learnable Aggregation Weights
di: Parsa, Javad, et al.
Pubblicazione: (2025)

Communication-Efficient Federated Learning with Adaptive Number of Participants
di: Skorik, Sergey, et al.
Pubblicazione: (2025)

How to Evaluate Participant Contributions in Decentralized Federated Learning
di: Anada, Honoka, et al.
Pubblicazione: (2025)

Stochastic Decision Horizons for Constrained Reinforcement Learning
di: Milosevic, Nikola, et al.
Pubblicazione: (2026)

Knowledge Rumination for Client Utility Evaluation in Heterogeneous Federated Learning
di: Jiang, Xiaorui, et al.
Pubblicazione: (2023)

Don't Reach for the Stars: Rethinking Topology for Resilient Federated Learning
di: Konstantin, Mirko, et al.
Pubblicazione: (2025)

VFL-RPS: Relevant Participant Selection in Vertical Federated Learning
di: Khan, Afsana, et al.
Pubblicazione: (2025)

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining
di: Sow, Daouda, et al.
Pubblicazione: (2025)

Federated Learning with Differential Privacy: An Utility-Enhanced Approach
di: Ranaweera, Kanishka, et al.
Pubblicazione: (2025)

Conflict-Averse Gradient Aggregation for Constrained Multi-Objective Reinforcement Learning
di: Kim, Dohyeong, et al.
Pubblicazione: (2024)

Separate Aggregation of Split Network for Personalized Federated Learning
di: Kang, Yunseok, et al.
Pubblicazione: (2026)

Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation
di: Wei, Ting, et al.
Pubblicazione: (2025)

Leveraging Function Space Aggregation for Federated Learning at Scale
di: Dhawan, Nikita, et al.
Pubblicazione: (2023)

Selective Aggregation for Low-Rank Adaptation in Federated Learning
di: Guo, Pengxin, et al.
Pubblicazione: (2024)

Lossless Privacy-Preserving Aggregation for Decentralized Federated Learning
di: Miao, Xiaoye, et al.
Pubblicazione: (2025)