AI ethics evidence index Search
Source-linked daily record
Evidence record 883 · automatically gathered

C2FL: Clustered Continual Federated Learning under Spatial and Temporal Drift

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

Record details

Published: 16 June 2026
Source: arXiv
Category: Research
Topics: Privacy · Environment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Privacy · Shared topic: Environment

IUU+DB: Tracking Illegal, Unreported, and Unregulated Fishing, Seafood Fraud, and Labor Abuse through LLM-driven Information Extraction

arXiv · 16 June 2026

Shared topic: Privacy · Shared topic: Environment

Governed Shared Memory for Multi-Agent LLM Systems

arXiv · 23 June 2026

Shared topic: Privacy · Shared topic: Environment

Trustworthy Smart Fabs via Professional Proxies: Scaling Safe and Sustainable by Design (SSbD) through Industrial Data Spaces

arXiv · 8 June 2026

Shared topic: Privacy · Shared topic: Environment

Recent advances in AI-based mobile robots for human companionship: survey

Artificial Intelligence Review · 29 June 2026

Shared topic: Privacy · Shared topic: Environment

Generative AI and Federated Learning for Intrusion Detection Systems: A Survey

arXiv cs.CR (AI security) · 1 July 2026

Shared topic: Privacy · Shared topic: Environment

Narrow surveillance: Preserving privacy in AI-based safety monitoring in public spaces

Big Data & Society · 6 July 2026

How to cite this record

ethics.ai (16 June 2026), “C2FL: Clustered Continual Federated Learning under Spatial and Temporal Drift,” evidence record 883, https://ethics.ai/record/883 (originally published by arXiv).

JSON

Use and limitations

This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.