Abstract: Phishing attacks remain a threat to users and organizations in every country on the planet. One problem with centralized phishing detection systems is the need for protection of data privacy and the system is becoming more complex as new types of attack occur. This research develops an improved phish discovering method based mostly on federated studying by processing plenty of person datasets privately to implement superior outcomes. Moreover, this approach allows for the combining of multiple local models across all user devices, leading to improved phishing detection results in comparison to single models while maintaining the privacy of raw data. When tested against varying datasets our system is shown to be superior by having better scalability, better adaptability to new threats, and better ability to protect user credentials.

Keywords: Phishing Detection, Federated Learning, Cybersquatting, Privacy Preservation, Decentralized Machine Learning.

Downloads: | DOI: 10.17148/IARJSET.2026.13430