Federated Learning (FL) is an emerging method to train machine learning models without the data getting centralized. By not centralizing the data, FL is compatible with security-conscious sectors like healthcare, finance, and IoT. However, despite this benefit, FL currently encounters three key issues hindering mainstream adoption, including high energy consumption during distributed training, the requirement for trust amongst the users, and the absence of good verifiability to ensure the result is proper and not adulterated.In the past few years, researchers have attempted to solve each of these problems individually. Initiatives under Green FL work towards minimizing the carbon and energy footprint. Blockchain-enabled solutions incorporate mechanisms for trust among clients as well as incentives. Cryptographic and auditing mechanisms allow for some extent of verifiability. The majority of the above works consider the problems in isolation. What is still absent is an integrated picture that examines their interplay, trade-offs, and the potential for common frameworks.This paper surveys 45 papers from 2021 to 2025 that relate to energy awareness, blockchain incorporation, or verifiability in FL. We categorise each paper with the straightforward coding scheme (Yes, Partial, No) on the three dimensions and study overlaps. The results show blockchain as the most progressed strand, energy-efficiency dealt with moderately, while verifiability remains the least studied. The paper ends with gaps, open issues, and future work towards sustainable and trustworthy FL.