Is there a secure way to share cyber threats knowledge among multiple organisations for a collective defense strategy ? Federated Learning (FL) has been introduced to enable collaboration among multiple organizations. In this paper, we propose a privacy-preserving federated learning approach to share cyber threat intelligence. The distributed nature of FL gives rise to different threats, such as inference attacks, poisoning attacks, and identity theft. In this work, we consider inference attacks, where a malicious subset of participant nodes aim to infer the training data of the victim. The attacker may generate and optimize vectors of features values to infer these data. We suggest a Federated Learning Approach Against Inference Attacks and Risks (FLAIR) where nodes collaboratively train models without a centralized server. We measured the data generated by the attacker against the real data of the target victim, and we show that the accuracy of this inference attack is low. We additionally conducted a set of experiments to test the performance and resilience of FLAIR. We then attended a high performance metrics with an accuracy around eighties.