Federated Coreset Subsampling for Distributed Memory Banks
In real-world scenarios, industrial processes can span multiple factories and goods are often produced at multiple sites, leading to a shared yet non-identical distribution of data across locations. To address this, we propose two federated approaches for i) combining existing coresets (Double Subsampling) and ii) creating new coresets (Federated Subsampling) in a distributed anomaly detection setting. In particular, we adapt the memory bank based PatchCore algorithm to a federated setting, resulting in increased cold-start performance, resilience to domain- and sensor-shift, and resilience to unreliable actor participation, a common problem in classical, network based federated learning ...
| Authors | Jason Witzemann, Daniel Pröll, Sebastian Hegenbart |
|---|---|
| Primary category | cs.DC |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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