The Internet of Things has begun to spread over a variety of domains, including industry and finance. It represents an increasing threat for both IT and OT. The lack of collaboration results in the same attacks targeting different organizations one after the other. Often employed as an answer to this problem, cyber threat-intelligence sharing induces its own set of challenges: trust, privacy, and traceability. This thesis takes advantages of a distributed sharing-oriented architecture and to enhance the security of industrial infrastructures. We study Federated Learning algorithms to build a distributed, autonomic system for detecting and characterizing attacks, as well as providing countermeasures. Experiments on real-world testbeds at the chair Cyber CNI allow us to validate the theoretical assumptions against realistic infrastructures and scenarios, fitting industrial use-cases.