arXiv:2302.13473v2 Announce Type: replace Abstract: Federated learning (FL) enables multiple data owners to build machine learning models collaboratively without exposing their private local data. In order for FL to achieve widespread adoption, it is important to balance the need for performance, privacy-preservation and interpretability, especially in mission critical applications such as finance and healthcare. Thus, interpretable federated learning (IFL) has become an emerging topic of research attracting significant interest from the academia and the industry alike. Its interdisciplinary n
The accelerating adoption of federated learning in sensitive domains like finance and healthcare necessitates solutions for trust and transparency, making interpretability a critical and timely research focus.
As AI moves into mission-critical applications, establishing interpretability while maintaining privacy in decentralized learning models is crucial for regulatory approval, public acceptance, and responsible deployment.
The focus on interpretable federated learning indicates a maturation of the FL field, moving beyond just performance and privacy to address trust and accountability, potentially broadening its applicability.
- · AI developers
- · Healthcare sector
- · Finance sector
- · Regulatory bodies
- · Opaque AI systems
- · Organizations with low data privacy standards
Increased trust and adoption of federated learning solutions in highly regulated industries.
Development of new regulatory frameworks specifically addressing the interpretability and privacy of distributed AI models.
Enhanced responsible AI development across various sectors, leading to more ethical and transparent autonomous systems.
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