Hi-SAFE: Hierarchical Secure Aggregation for Lightweight Federated Learning

A recent study has proposed enhancements to zero-shot recognition of Activities of Daily Living (ADLs) using Large Language Models (LLMs) by implementing event-based segmentation and a novel method for estimating prediction confidence. This approach aims to improve the accuracy of sensor-based recognition systems in smart homes, which are crucial for applications in healthcare and safety management.

A new study presents a lookup table (LUT) compilation pipeline for Kolmogorov-Arnold Networks (KANs), enhancing Denial-of-Service (DoS) detection on resource-constrained Internet of Things (IoT) edge devices. This approach replaces costly spline computations with precomputed tables, significantly reducing inference latency while maintaining high detection accuracy of 99.0% on the CICIDS2017 dataset.

A recent study introduces a novel approach to federated ridge regression, demonstrating that iterative communication between clients and a central server is unnecessary for achieving exact recovery of the centralized solution. By aggregating sufficient statistics from clients in a single transmission, the server can reconstruct the global solution through matrix inversion, significantly reducing communication overhead.

Recent research highlights a new type of attack on Federated Learning (FL) that compromises the fairness of trained models, revealing that controlling just one client can skew performance distributions across various attributes. This raises concerns about the integrity of models in sensitive applications where fairness is critical.