Federation over Text: Insight Sharing for Multi-Agent Reasoning

2026-04-18 · Dixi Yao, Tahseen Rabbani, Manzil Zaheer, Tian Li arxiv

We propose a federated learning-like framework, Federation over Text (FoT), that enables multiple clients solving different tasks to collectively generate a shared library of metacognitive insights by iteratively federating their local reasoning processes without sharing actual problem instances or task instructions. Instead of federation over gradients (e.g., as in distributed training), FoT operates at the semantic level without any gradient optimization or supervision signal. Iteratively, each client runs an LLM agent that does local thinking and self-improvement on their specific tasks independently, and shares reasoning traces with a central server, which aggregates and distills them into a cross-task (and cross-domain) insight library that existing and future agents can leverage to improve performance on related tasks. Experiments show that FoT improves reasoning effectiveness and efficiency across a wide range of challenging applications, including mathematical problem solving, cross-domain collaboration, real-world daily tasks, and machine learning research insight discovery. Specifically, it improves average performance scores by 25% while reducing the reasoning tokens by 4% across the first three applications. In the research insight discovery application, FoT is able to generate insights that cover over 80% of the major contributions in the subsequent papers.

📄 PDF Abstract BibTeX arXiv:2604.16778

Code (0)

등록된 구현이 없습니다.

Tasks

Federated Learning

Cloud-Based Federation Framework and Prototype for Open, Scalable, and Shared Access to NextG and IoT Testbeds

2024-08-26 · Maxwell McManus, Tenzin Rinchen, Annoy Dey, Sumanth Thota 외

In this work, we present a new federation framework for UnionLabs, an innovative cloud-based resource-sharing infrastructure designed for next-generation (NextG) and Internet of Things (IoT) over-the-air (OTA) experiment…

On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systems

2026-05-08 · Usevalad Milasheuski, Piero Baraldi, Enrico Zio, Stefano Savazzi arxiv

Federated Learning (FL) has emerged as a promising paradigm for preserving client data ownership and control over distributed Internet of Things (IoT) environments. While discriminative models dominate most FL use cases,…

Unsupervised Anomaly Detection Time Series Analysis Federated Learning

FedFormer: Contextual Federation with Attention in Reinforcement Learning

2022-05-27 · Liam Hebert, Lukasz Golab, Pascal Poupart, Robin Cohen

A core issue in multi-agent federated reinforcement learning is defining how to aggregate insights from multiple agents. This is commonly done by taking the average of each participating agent's model weights into one co…

Federated Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Evidential Federated Learning for Skin Lesion Image Classification

2024-11-15 · Rutger Hendrix, Federica Proietto Salanitri, Concetto Spampinato, Simone Palazzo 외

We introduce FedEvPrompt, a federated learning approach that integrates principles of evidential deep learning, prompt tuning, and knowledge distillation for distributed skin lesion classification. FedEvPrompt leverages …

Classification Federated Learning image-classification Image Classification+3

Multi-Task Learning as enabler for General-Purpose AI-native RAN

2024-04-05 · Hasan Farooq, Julien Forgeat, Shruti Bothe, Kristijonas Cyras 외

The realization of data-driven AI-native architecture envisioned for 6G and beyond networks can eventually lead to multiple machine learning (ML) workloads distributed at the network edges driving downstream tasks like s…

Multi-Task Learning Prediction