arXiv:2505.23593v4 Announce Type: replace Abstract: Post-training of foundation language models has emerged as a promising research domain in federated learning (FL) with the goal to enable privacy-preserving model improvements and adaptations to user's downstream tasks. Recent advances in this area adopt centralized post-training approaches that build upon black-box foundation language models where there is no access to model weights and architecture details. Although the use of black-box models has been successful in centralized post-training, their blind replication in FL raises several con
The proliferation of foundation models and growing concerns about data privacy and control are driving federated learning research toward decentralized post-training methods.
This development highlights the technical feasibility and strategic advantages of using open-source models in federated AI, which can democratize access and reduce dependency on monolithic AI providers.
The focus is shifting from black-box foundation models to open-source architectures for federated post-training, necessitating new design principles and collaboration models.
- · Open-source AI communities
- · Organizations prioritizing data privacy
- · Developers of federated learning frameworks
- · Proprietary black-box AI model providers
- · Centralized model training platforms
- · Users relying on opaque AI systems
More robust and privacy-preserving AI systems emerge through collaborative training on open architectures.
Increased adoption of open-source AI models as their capabilities improve through federated fine-tuning, potentially reducing vendor lock-in.
A fragmentation of AI model development, with specialized federated communities building and iterating on open-source foundations for specific use cases.
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