arXiv:2603.14198v3 Announce Type: replace Abstract: Deploying trustworthy AI systems requires principled uncertainty quantification. Conformal prediction (CP) is a widely used framework for constructing prediction sets with distribution-free coverage guarantees. In many practical settings, including healthcare, finance, and mobile sensing, the calibration data required for CP are distributed across multiple clients, each with its own local data distribution. In this federated setting, data can often be partitioned into, potentially overlapping, groups, which may reflect client-specific strata
The increasing deployment of AI systems in sensitive applications like healthcare and finance necessitates robust uncertainty quantification, driving research into methods like federated conformal prediction.
This development allows AI systems to provide reliable prediction sets with coverage guarantees even when data is distributed and heterogeneous, which is crucial for trustworthy AI in privacy-sensitive and multi-client environments.
The ability to deploy trustworthy AI with quantifiable uncertainty in federated settings lowers barriers for adoption in regulated industries and where data privacy is paramount.
- · Healthcare providers leveraging AI
- · Financial institutions using AI for risk assessment
- · AI developers focused on privacy and trustworthiness
- · Sectors with sensitive or distributed data
- · AI solutions lacking uncertainty quantification
- · Centralized AI models for multi-client data
- · Legacy systems with opaque decision-making
Increased adoption of federated learning and trustworthy AI in regulated industries due to enhanced reliability.
Development of industry standards and regulatory frameworks that mandate certain levels of uncertainty quantification for AI deployments.
Enhanced public trust in AI systems leads to broader societal integration of AI, potentially accelerating automation with strong ethical safeguards.
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Read at arXiv cs.LG