arXiv:2606.10595v1 Announce Type: cross Abstract: Federated Learning (FL) has emerged as a promising solution for data hunger in centralized learning. This paradigm enables privacy with multiple clients to train a shared-task model collaboratively without exposing their local data. While being a key component in any learning system, data is also a primary source of vulnerabilities and challenges, and a major determinant of a stable and well-converged training. Existing FL reviews describe general foundations, security practices, opportunities, challenges, and applications, without delving into
The increasing push for data privacy and the limitations of centralized data processing in AI development are driving the exploration of federated learning solutions.
Federated Learning addresses the critical challenge of data scarcity and privacy concerns, enabling collaborative AI model training without compromising sensitive local data, which is crucial for regulated industries and national AI strategies.
This approach shifts the paradigm of AI development from centralized data hoards to distributed, privacy-preserving collaboration, impacting data governance, security protocols, and model robustness.
- · Healthcare sector
- · Financial institutions
- · Governments
- · AI algorithm developers
- · Companies reliant on centralized data exploitation
- · Traditional data brokers
- · Entities with weak data privacy frameworks
Wider adoption of privacy-preserving AI technologies accelerates, mitigating data exposure risks.
New regulatory frameworks emerge to standardize Federated Learning deployments and ensure accountability across distributed datasets.
The development of 'data unions' or collaborative data trusts could decentralize AI power further, challenging large tech monopolies.
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Read at arXiv cs.AI