  The integration of Foundation Models (FMs) with Federated Learning (FL)
presents a transformative paradigm in Artificial Intelligence (AI), offering
enhanced capabilities while addressing concerns of privacy, data
decentralization, and computational efficiency. This paper provides a
comprehensive survey of the emerging field of Federated Foundation Models
(FedFM), elucidating their synergistic relationship and exploring novel
methodologies, challenges, and future directions that the FL research field
needs to focus on in order to thrive in the age of foundation models. A
systematic multi-tiered taxonomy is proposed, categorizing existing FedFM
approaches for model training, aggregation, trustworthiness, and
incentivization. Key challenges, including how to enable FL to deal with high
complexity of computational demands, privacy considerations, contribution
evaluation, and communication efficiency, are thoroughly discussed. Moreover,
the paper explores the intricate challenges of communication, scalability and
security inherent in training/fine-tuning FMs via FL, highlighting the
potential of quantum computing to revolutionize the training, inference,
optimization and data encryption processes. This survey underscores the
importance of further research to propel innovation in FedFM, emphasizing the
need for developing trustworthy solutions. It serves as a foundational guide
for researchers and practitioners interested in contributing to this
interdisciplinary and rapidly advancing field.
