WiMoT is a replay‑free continual Wi‑Fi sensing framework that stores per‑class feature means and covariances instead of raw samples, allowing it to adapt to new rooms or users while preserving earlier knowledge. It restricts representation changes to an affine map on a frozen feature basis, ensuring stored statistics remain valid without old data. Experiments on 3DO, WiMANS, and Widar3.0 show WiMoT achieves high final accuracies (up to 95.16%) with minimal forgetting (≤0.58 pp) and outperforms 21 baselines, including those that use replay, while keeping memory usage low by compressing covariances to rank‑32 factors.
arXiv:2608. 14670v1 Announce Type: cross Abstract: Passive, device-free person identification offers an alternative to camera- and wearable-based biometrics, yet existing wireless approaches rely largely on gait or activity cues and are rarely evaluated at scale.
CSI-JEPA is a self‑supervised framework that learns reusable temporal‑spectral representations from unlabeled Wi‑Fi channel state information (CSI) by predicting latent features of masked channel regions. It tokenizes CSI along time and subcarrier dimensions and uses a channel‑variation‑aware masking strategy to focus on dynamic regions. After pretraining, the frozen encoder is adapted with lightweight adapters to a variety of downstream Wi‑Fi sensing tasks, achieving significant accuracy gains and label savings compared to supervised baselines.
arXiv:2606. 31495v1 Announce Type: new Abstract: We study a single idea across two settings: that a prediction-error signal, computed by a small predictor over the latent space of a frozen encoder, can serve both as a gate on plasticity and as a substrate for metacognition.
arXiv:2606. 29465v1 Announce Type: new Abstract: Class-incremental learning requires a model to learn new classes while preserving decision regions for old ones.
The paper proposes a drift‑adaptive method for Domain‑Incremental Learning that tracks representation drift for each past domain‑class pair. By measuring how much a concept’s current representation deviates from a fixed anchor, the method assigns stronger preservation to high‑drift pairs and down‑weights highly drifted domains during prototype construction. Experiments on DomainNet, Office‑Home, CORe50, and CDDB‑Hard show that this approach maintains a strong accuracy‑forgetting balance while using less training memory and the smallest storage compared to other methods.
arXiv:2606. 03939v1 Announce Type: cross Abstract: Federated Learning (FL) of foundation and edge models increasingly targets deployments where client data distributions drift over time, yet existing forgetting-mitigation methods assume each client's distribution is stationary.
The paper investigates how the time cost of 802.11 contention affects federated learning. Using ns-3 simulations, the authors measure frame-delivery ratios and saturation throughput across various client densities and offered loads, then employ a FedAvg trainer that uses these ratios to estimate communication time. Across 720 runs with diverse datasets, partitions, densities, loads, and seeds, all models reached target accuracy within the round budget, with communication time-to-target increasing significantly as client density rose. The study also compares uniform and persistent heterogeneous participation, finding no statistically significant accuracy gap, though confidence intervals are wide. The results are specific to the evaluated configurations and do not generalize to all convergence or fairness scenarios.
The paper introduces KVLearn, a learning‑based framework for managing key‑value (KV) cache retention in disaggregated large language model (LLM) serving systems. KVLearn comprises a Prefix Reuse Predictor that estimates reuse probability from prefix features, a Cost‑Aware Retention Score that translates this probability into keep/admit decisions based on recomputation, transfer, and storage costs, and an Adaptive Threshold Controller that tunes admission thresholds online. Integrated into a globally disaggregated serving topology, KVLearn achieves up to 56% faster time‑to‑first‑token and up to 53% reduction in inter‑node KV transfer compared to baseline caching strategies.
The paper introduces RoCo-FL, a robust and convergent framework for Over-the-Air Federated Learning that addresses channel noise, fading, and data/system heterogeneity. RoCo-FL models local updates with inexact optimization and a proximal constraint to stabilize updates under wireless impairments and stragglers. The authors provide convergence guarantees for non-convex objectives and demonstrate improved stability and accuracy on CIFAR10, CIFAR100, and FEMNIST compared to existing baselines.
arXiv:2608. 04050v1 Announce Type: cross Abstract: Wi-Fi networks are moving beyond random channel access toward tightly coordinated operation across access points (APs), a shift reflected in Wi-Fi 8's multi-AP coordination (MAPC).
The paper investigates what information must be preserved in replay buffers for class‑incremental learning. By treating cached predictions as temporally heterogeneous supervision, the authors separate classes known at storage time from those learned later, and evaluate the impact of deleting logit matching. Experiments on CIFAR‑100 with DER++ show that a simple task‑level offset can largely correct the cost of removing later‑class matching, while the cost of disrupting class correspondence remains.
Federated Learning (FL) of foundation and edge models increasingly targets deployments where client data distributions drift over time, yet existing forgetting-mitigation methods assume each client's distribution is stationary. Flashback, the strongest recent FL method against cross-client (spatial) forgetting, uses monotonically accumulating per-class label counts as a knowledge proxy; this proxy becomes miscalibrated under temporal distribution shift and anchors the global model to an outdated class balance.