arXiv:2606.18003v1 Announce Type: cross Abstract: Collective Adaptive Systems (CAS) increasingly rely on machine learning to let each node learn from locally sensed data, aligning its behavior with the surrounding environment. Scaling this intelligence, however, raises fundamental challenges: sensed data is often privacy-sensitive, preventing centralized collection; nodes are mobile, traversing regions where nearby nodes perceive similar phenomena while distant ones observe radically different conditions, creating natural spatial clusters; and these distributions evolve over time due to mobili
The increasing prevalence of decentralized AI systems, particularly in sensitive or mobile contexts, necessitates advanced federated learning techniques that can adapt to dynamic data distributions.
This work addresses fundamental challenges in scaling distributed AI, specifically concerning data privacy, spatial variability, and temporal drift, which are critical for robust and ethical AI deployment in complex, real-world environments.
The proposed C2FL framework offers a method for continual federated learning that can maintain model accuracy and relevance in scenarios marked by both fluid data distributions and privacy concerns, moving beyond static federated learning paradigms.
- · Edge AI providers
- · Privacy-preserving AI developers
- · Smart city infrastructure
- · Mobile computing platforms
- · Centralized data processing models
- · AI systems with static model retraining cycles
- · Traditional federated learning approaches
More resilient and adaptive AI models can be deployed in decentralized and privacy-sensitive sectors.
This could accelerate the adoption of AI in applications requiring on-device learning across diverse and changing environments.
It may lead to new regulatory frameworks for distributed AI, balancing privacy against the need for system-wide intelligence.
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Read at arXiv cs.AI