Deepfake technology employs advanced artificial intelligence to craft counterfeit videos, images, or audio that appear remarkably authentic, raising concerns about misinformation, privacy violations, and erosion of societal trust. This chapter explores innovative methods for identifying deepfakes using Federated Learning (FL) and blockchain, which collaborate to deliver secure, confidential, and adaptable detection solutions. With FL, devices such as smartphones train detection models locally without disclosing personal data, while blockchain securely logs model updates and outcomes to prevent manipulation. These strategies address challenges like safeguarding data, processing vast content volumes, and detecting sophisticated deepfakes. Applications span verifying media authenticity, securing financial transactions, ensuring healthcare integrity, promoting transparent governance, and protecting social media. This chapter also examines ethical dilemmas, such as biased model performance, and regulatory obstacles impacting deployment. It provides a strategic roadmap for deepfake detection, aiming to assist researchers, industry professionals, and policymakers in developing equitable, dependable solutions for a safer digital ecosystem.