Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights
arXiv:2411.18343v3 Announce Type: replace Abstract: Personalized Federal learning(PFL) allows clients to cooperatively train a personalized model without disclosing their private dataset. However, PFL suffers from Non-IID, heterogeneous devices, lack of fairness, and unclear contribution which urgently need the interpretability of deep learning model to overcome these challenges. These challenges proposed new demands for interpretability. Low cost, privacy, and detailed information. There is no current interpretability method satisfying them. In this paper, we propose a novel interpretability
The paper addresses the urgent demand for interpretable AI models in the context of personalized federated learning, which is gaining prominence for its privacy-preserving benefits but faces significant challenges.
Improved interpretability methods that are low-cost, private, and detailed are crucial for deploying reliable and trustworthy AI systems, especially in sensitive domains like personalized federated learning.
The development of a novel interpretability method addressing cost, privacy, and detail will enhance the practical application and adoption of personalized federated learning models, making them more transparent and auditable.
- · AI researchers
- · Federated learning platforms
- · Sectors requiring explainable AI
- · Non-interpretable AI solutions
The new method provides a clearer understanding of how federated learning models make decisions, addressing critical challenges like non-IID data and device heterogeneity.
Increased trust and adoption of federated learning in various industries due to enhanced transparency and accountability.
Accelerated development of more sophisticated and provably fair AI agents capable of operating in privacy-sensitive and distributed environments.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG