Year: 2025
Type: conference-paper
Abstract: The recent development of Web Intelligence has heightened privacy concerns among end-users. Federated intelligence offers a novel approach to restructuring Web Intelligence within a federated setting to better protect privacy. Additionally, the advent of large foundation models has notably enhanced the capability of individual agents to address complex problems and has reshaped the Web ecosystem. Numerous large language models and domain-specific foundation models underpin various applications, linking end-users to the connected Web. This tutorial presents federated intelligence as a strategy to develop a Web-based collective intelligence system by leveraging existing foundations and applications. In this framework, agents collaboratively enhance their intelligence by acquiring complementary knowledge and making fine-grained adaptations, enabling them to manage complex tasks across diverse web environments. The tutorial provides an in-depth review of recent advancements and potential directions in federated intelligence, detailing its core concepts, applications, and future developments. It is designed for scholars, practitioners, and interested audiences, offering a concise overview that facilitates quick understanding and encourages meaningful discussions on its future evolution.
Authors: Peng Yan, Shengchao Chen, Guodong Long
Institution: University of Technology Sydney
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: University of Technology Sydney
OpenAlex ID: W4410637818