Recent Advances in the Synergy Between Federated Learning and Foundation Models
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Deadline for manuscript submissions: 31 January 2025 | Viewed by 96
Special Issue Editors
Interests: federated learning; edge AI; wireless communications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Foundation models (FMs), such as the Generative Pre-trained Transformer (GPT) series, are large generative models that are competent in a variety of tasks. They have become the key enablers for many AI applications, including chatbots, image captioning, and video editing. However, the versatility and generalizability of FMs make their training highly difficult, which demands massive datasets and tremendous computational resources. This creates significant obstacles including scalability, privacy, and efficiency concerns in real-world use cases. As the most popular framework of privacy-preserving collaborative training, federated learning (FL) is believed to continue to play an important role in the age of FMs. Recently, the generative power of FMs has also been found effective in overcoming some open challenges of FL for improved performance and better personalization.
This Special Issue solicits original research and review articles, aiming to bring together researchers, practitioners, and industry experts from around the world to explore the latest advancements, deployment challenges, and opportunities in synergizing FL and FMs.
Dr. Yuyi Mao
Dr. Jiawei Shao
Guest Editors
Manuscript Submission Information
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Keywords
- federated learning
- foundation models
- large language models
- multimodal models
- generative AI
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