Tsetlin Machine (TM) is a rule-based machine-learning algorithm comprising collectives of two-action Tsetlin Automata (TAs) that cooperatively form conjunctive logical clauses from Boolean inputs through stochastic feedback. Although few recent studies have examined TM Federated Learning, the broader area of distributed and decentralized TM learning has not received much attention in the existing literature and warrants further exploration.
arXiv:2505. 19699v2 Announce Type: replace-cross Abstract: Federated Learning (FL) is a decentralized machine learning paradigm that enables clients to collaboratively train models while preserving data privacy.
By Junming Liu, Yanting Gao, Yuqi Li, Siyuan Meng, Yifei Sun, Aoqi Wu, Yirong Chen, Ding Wang, Shiping Wen
arXiv:2609.35914v1 Announce Type: cross
Abstract: Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, making it suitable for privacy-sens...
By Deepthy K. Bhaskar, VP Binu, B Minimol
arXiv:2606. 12281v1 Announce Type: cross Abstract: In Decentralized Training and Decentralized Execution (DTDE) for cooperative Multi-Agent Reinforcement Learning (MARL), action-advising-based knowledge sharing promotes interpretable and scalable cooperation among agents.
By Jinyuan Zu, Xiaowei Lv, Yongcai Wang, Deying Li, Yunjun Han, Wenping Chen, Fengyi Zhang, Naiqi Wu
arXiv:2609.07312v1 Announce Type: new
Abstract: This paper proposes a robust decentralized personalized federated learning method R-DPFL, that enables clients to reduce the impact of Byzantine attack...
By Xiao Ma, Hong Shen, Hui Tian, Wenqi Lyu, Wei Ke
arXiv:2607. 08368v1 Announce Type: new Abstract: With the widespread deployment of basic models in edge intelligence, communication bandwidth has become a core bottleneck restricting the scalability of federated learning.
By Lingyu Qiu, Daniela Annunziata, Stefano Izzo, Fabio Giampaolo, Francesco Piccialli
With the widespread deployment of basic models in edge intelligence, communication bandwidth has become a core bottleneck restricting the scalability of federated learning. Although one-shot federated learning alleviates this problem by minimizing communication rounds, existing iterative fine-tuning or knowledge distillation methods still face challenges such as high server-side computational costs and hyperparameter sensitivity.
arXiv:2608. 12108v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training across distributed clients while keeping data local.
By Mirko Konstantin, Stefan Zachow, Anirban Mukhopadhyay
The paper introduces a latent information sharing scheme for federated learning that mitigates client drift by sharing a small amount of hidden‑layer activations. The authors demonstrate both theoretically and empirically that this approach improves training efficiency while maintaining convergence guarantees and data privacy. Compared to existing methods such as FedProx, SCAFFOLD, FedPVR, FedProto, and SplitFed, the proposed method achieves higher model accuracy within a fixed round budget without adding significant communication overhead.
By Seungjun Lee, Ensieh Khazaei, Dimitrios Hatzinakos, Baturalp Buyukates, Sunwoo Lee
arXiv:2606. 28217v1 Announce Type: cross Abstract: We propose a framework for reward allocation in fully delegated AI cooperatives where humans are represented by agents that contribute data and participate in model updates under heterogeneous value constraints.
By Young Yoon, Jimin Kim, Soyeon Park
arXiv:2601. 09304v2 Announce Type: replace Abstract: Federated Learning (FL) enables distributed learning across multiple clients without sharing raw data.
By Sota Sugawara, Yuji Kawamata, Akihiro Toyoda, Tomoru Nakayama, Yukihiko Okada
The paper proposes a new federated learning approach called FedALS that reduces communication costs by varying aggregation frequencies across model layers. It derives tighter generalization bounds for one‑round and multi‑round federated learning, linking these bounds to local updates and data heterogeneity. Based on representation‑learning insights, the authors argue that infrequent aggregation of early layers and more frequent aggregation of final layers yields more generalizable models, especially in non‑iid settings, and demonstrate the method’s effectiveness experimentally.
By Peyman Gholami, Hulya Seferoglu