ICML 2025 Workshop on Collaborative and Federated Agentic Workflows

Large language models (LLMs) have rapidly evolved into powerful engines capable of driving agentic workflows, i.e., autonomous sequences of actions traditionally performed by humans (e.g., booking flights, preparing administrative forms) based on textual and/or visual inputs. Embracing collaborative and federated learning is essential in this context, as these paradigms enable the aggregation of distributed data while preserving user privacy and ensuring regulatory compliance. By keeping data localized, federated approaches allow agentic workflows to continuously learn and adapt from diverse user interactions without exposing sensitive information. This distributed learning framework not only facilitates scalable and personalized improvements but also mitigates biases by incorporating insights from a broad range of environments, ultimately amplifying the transformative potential of agentic workflows for both industry and everyday applications.

Recent commercial deployments, such as OpenAI Operator, highlight the significant impact of agentic workflows on the global economy and daily life. However, these workflows currently face several challenges including imprecise execution (e.g., incorrectly interacting with UI elements), suboptimal tool-use efficiency (e.g., latency in processing), and limitations in adaptive user-agent interactions (e.g., ineffective co-piloting and supervision). Additionally, while agentic workflows generate valuable data from user interactions, the sensitive and localized nature of this data creates hurdles for centralized learning approaches.

Collaborative and federated learning are powerful methodologies to overcome these challenges. They facilitate collective improvement by enabling continuous workflow optimization through the distributed updates of the model and prompts without having to share the raw data. These methods also support personalization by tailoring agentic responses to individual user styles and preferences without compromising privacy. Importantly, they maintain strict regulatory compliance by ensuring that sensitive data remains local, which a critical requirement under emerging legislative frameworks such as the EU AI Act and Canada Bill C-27.

This workshop uniquely focuses on the convergence of collaborative/federated learning with agentic workflows, fostering interdisciplinary research that bridges theoretical foundations, practical implementations, and regulatory considerations.