TMLR

FedDUAL: A Dual-Strategy with Adaptive Loss and Dynamic Aggregation for Mitigating Data Heterogeneity in Federated Learning

FedDUAL: 一种具有自适应损失和动态聚合的双策略，用于缓解联邦学习中的数据异质性

Pranab Sahoo, Ashutosh Tripathi, Sriparna Saha, Samrat Mondal

机构 * Indian Institute of Technology Patna（印度理工学院帕纳瓦分校） 纠错

AI总结 FedDUAL通过自适应损失和动态聚合策略，有效缓解联邦学习中的数据异质性问题，提升模型性能和鲁棒性。

Comments Transactions on Machine Learning Research (TMLR)

Policy-Guided Search on Tree-of-Thoughts for Efficient Problem Solving with Bounded Language Model Queries

基于树状思维的策略引导搜索以实现高效的有限语言模型查询问题解决

Sumedh Pendurkar, Guni Sharon

机构 * Texas A&M University（德克萨斯大学） 纠错

AI总结 本文提出LTS算法，通过语言模型的启发式概率引导树状思维搜索，以在有限计算预算下提升问题解决效率。

Comments Published in Transactions on Machine Learning Research (TMLR), 2025. Available at https://openreview.net/forum?id=Rlk1bWe2ii

Journal ref Transactions on Machine Learning Research, 2025