Federated Coreset Subsampling for Distributed Memory Banks

Scientific discovery increasingly relies on federated learning over data from high-resolution instruments, and these data are strongly non-IID and long-tailed. Coreset selection could shorten training, but existing federated coreset methods either prune with local heuristics that ignore the global class distribution, or first train a warm-up model at every client and upload class centroids of its features. This makes coreset selection a distributed bottleneck that consumes GPU time at every client, grows with the model and the number of clients, and must be repeated for every architecture. We introduce SCOPE, named for Semantic Coreset using Orthogonal Projection Embeddings, a scalable, training-free federated coreset framework for long-tailed data. Each client scores its samples once with a frozen, compact vision-language model and uploads scalar statistics per class. The server merges these into a global class profile, which lets each client remove anomalies and prune redundant samples only from classes that are locally abundant but globally common. SCOPE therefore requires no warm-up training, needs a single exchange with the server, and uploads a payload that depends only on the number of classes. On GPU nodes of an HPC cluster, with four datasets, including scientific micrographs, and six baselines, SCOPE selects its coreset up to 7.7x faster and uploads up to 293x less data than the feature-based baseline. As the number of clients grows from 10 to 500, SCOPE's time before training grows by fewer seconds than any baseline's, and at 500 clients the baselines need 1.7 to 3.6x SCOPE's time. Trained on the coreset, the global model reaches 98% of the full-data accuracy in 18 to 42% fewer global rounds than full-data training, with 8 to 21% less client computation over 200 rounds.

Greedy sampling produces a compact yet representative summary of normal data, which is essential for reliable anomaly detection that relies on measuring distance from normality. For continual anomaly detection where tasks arrive sequentially, extending greedy sampling is straightforward with unbounded memory through coreset accumulation. However, practical deployment requires fixed memory where the coreset size remains constant regardless of task count. We observe that continued greedy sampling, which iteratively applies greedy selection over previously greedy-sampled sets, effectively preserves representativeness under strict memory limits. Despite discarding data at each step to satisfy the memory constraint, coreset quality degrades gracefully rather than catastrophically, enabling reliable anomaly detection across the tasks. We provide theoretical justification by showing that resulting greedy-continued coreset approximates the oracle coreset within a bounded gap. We instantiate this principle in ContCore, which constructs a greedy-continued coreset through greedy expansion on new task features followed by greedy consolidation to enforce the memory budget. Unlike neural methods susceptible to catastrophic forgetting or naive coreset accumulation requiring unbounded memory, ContCore maintains fixed memory with theoretical guarantees. Empirically, ContCore achieves state-of-the-art performance across 11 task schedules on MVTecAD and VisA, and extends effectively to online continual AD settings where prior methods degrade significantly. Code: https://github.com/jungyg/ContCore

Distributed IoT systems generate multivariate time-series streams for monitoring physical assets, servers, and embedded sensing platforms. Detecting abnormal temporal behavior is critical for fault diagnosis, predictive maintenance, and security. However, practical IoT anomaly detection is hindered by decentralized and non-IID data, limited bandwidth, and the constrained computation and memory of edge devices. This paper proposes FedKAD, a resource-efficient federated Koopman anomaly detection framework for distributed IoT multivariate time series. Unlike deep-learning-based anomaly detectors that require training and communicating large neural models, FedKAD learns normal temporal dynamics through lightweight sliding-window Koopman representations. Federated training is formulated as a low-rank consensus problem, where raw sensor streams and local reduced dynamics remain on device while only compact subspace variables are exchanged with the server. To optimize the shared representation under orthonormality constraints, we develop a federated Stiefel-ADMM algorithm and provide convergence and stationarity analysis under partial client participation. During inference, each client detects anomalies locally by measuring the prediction residual between observed future trajectories and the learned Koopman dynamics. Experiments on four widely used multivariate time-series anomaly detection benchmarks show that FedKAD maintains or improves detection performance compared with federated deep-learning baselines. More importantly for IoT deployment, FedKAD provides up to $2.1\times10^3$ faster training, $80\times$ lower communication, and $79\times$ lower inference latency than neural baselines, confirming its suitability for resource-constrained edge devices.