We study streaming federated learning with limited client memory, where newly generated training data incur time-varying sampling costs and must be selectively admitted and retained over time. We consider a joint server-side admission and client-side memory-management framework with the objective of minimizing the cumulative excess population risk under a sampling-cost budget and buffer constraints.
arXiv:2608. 07157v1 Announce Type: new Abstract: Sub-model federated learning lets resource-constrained clients train width-reduced versions of a global model, but existing methods allocate capacity by device resources alone.
By Alireza Moayedikia, Alicia Troncoso Lora
arXiv:2606. 08452v1 Announce Type: new Abstract: In many real-world settings, data streams are nonstationary and arrive sequentially, requiring learning systems to adapt continuously without retraining from scratch.
By Nazreen Shah, Govinda Arya, Bharath B. N., Ranjitha Prasad
arXiv:2601. 19788v2 Announce Type: replace Abstract: Federated Continual Learning (FCL) leverages inter-client collaboration to better balance new knowledge acquisition and old knowledge retention on non-stationary data.
By Sixing Tan, Xianmin Liu
arXiv:2606. 08854v1 Announce Type: cross Abstract: Standard Reinforcement Learning with Verifiable Rewards (RLVR) training allocates a fixed rollout budget to every query, without regard for what each query's difficulty means for the current policy.
By Shivchander Sudalairaj, Kai Xu, Akash Srivastava, Giorgio Giannone
arXiv:2606. 17805v1 Announce Type: new Abstract: Data acquisition is a major bottleneck for learning in real-time streams: analysts must decide on the fly which labels to purchase while respecting a rolling budget.
By Xiwen Huang, Pierre Pinson
arXiv:2408. 05886v5 Announce Type: replace Abstract: Heterogeneous system configurations of distributed clients connected to the central server (CS) via a time-varying wireless network pose significant challenges for popular distributed machine learning (ML) algorithms such as federated learning (FL).
By Ferdous Pervej, Minseok Choi, Andreas F. Molisch
arXiv:2501. 12942v2 Announce Type: replace Abstract: Effective multi-user delay-constrained scheduling is crucial in various real-world applications, including embodied AI, instant messaging, live streaming, and data center management, where efficient resource allocation is required among users with diverse delay sensitivities.
By Zhuoran Li, Ruishuo Chen, Hai Zhong, Longbo Huang
arXiv:2508. 05157v2 Announce Type: replace Abstract: Federated Learning (FL) enables collaborative training across distributed clients without sharing raw data, offering strong privacy benefits.
By Thinh Nguyen, Le Huy Khiem, Van-Tuan Tran, Khoa D Doan, Nitesh V Chawla, Kok-Seng Wong
arXiv:2506. 20573v4 Announce Type: replace-cross Abstract: Public datasets, crucial for modern machine learning and statistical inference, often contain low-quality or contaminated samples that can harm model performance.
By Kristian Minchev, Dimitar I. Dimitrov, Nikola Konstantinov
arXiv:2603. 05774v2 Announce Type: replace Abstract: This paper addresses the distributed stochastic minimax optimization problem subject to stochastic constraints.
By Zhankun Luo, Antesh Upadhyay, Sang Bin Moon, Abolfazl Hashemi
arXiv:2512. 12816v2 Announce Type: replace Abstract: We study how to allocate resources for training and deployment of machine learning (ML) models under concept drift and limited budgets.
By Hasan Burhan Beytur, Haris Vikalo, Kevin S Chan, Gustavo de Veciana