The content discusses the limitations of traditional Federated Learning (FL) systems and proposes the concept of Open Federated Learning Platforms. It explores two cooperation frameworks, query-based FL and contract-based FL, emphasizing legal compliance, model licensing, and knowledge sharing within models.
Traditional FL systems are criticized for their server-client coupling, low model reusability, and lack of public accessibility. The proposed Open FL platforms aim to address these issues by promoting collaborative machine learning infrastructure for all Internet users. The article highlights the importance of legal considerations in batch model reuse mechanisms to ensure responsible use and intellectual property protection.
The discussion covers various licenses for ML models available on platforms like Hugging Face, outlining rights, restrictions, and enforcements. It also delves into batch model reuse mechanisms such as combination, amalgamation, distillation, and generation. The content emphasizes the need for selecting appropriate licenses to facilitate knowledge aggregation while ensuring legal compliance.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Moming Duan,... at arxiv.org 03-01-2024
https://arxiv.org/pdf/2307.02140.pdfDeeper Inquiries