arXiv:2604.16778v2 Announce Type: replace Abstract: 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 think
The proliferation of advanced LLMs and the increasing complexity of multi-agent systems necessitate new paradigms for collaborative intelligence without compromising data privacy or proprietary information.
This framework offers a novel approach to scaling AI capabilities and knowledge sharing across diverse, sensitive domains by enabling semantic-level federation rather than traditional data or gradient sharing.
AI agents can now collaboratively learn and share insights without directly exposing their raw data or task specifics, significantly altering how distributed AI systems can be designed and deployed.
- · Organizations with sensitive data
- · Multi-agent system developers
- · Decentralized AI platforms
- · LLM developers
- · Centralized data aggregators
- · AI models requiring extensive direct data sharing
Increased efficiency and knowledge transfer in multi-agent AI systems and federated learning applications.
Accelerated development of AI solutions for highly regulated industries due to enhanced privacy-preserving collaboration.
The emergence of new AI-driven service models that leverage distributed 'metacognitive' insights across independent entities, potentially leading to unprecedented levels of collective AI intelligence.
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