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This paper presents a comprehensive experimental comparison of various federated learning aggregation strategies, analyzing their performance and efficiency under both homogeneous and heterogeneous data distributions.
The open-source project RuView leverages Wi-Fi signals and AI technology to achieve camera-free through-wall sensing, capable of real-time human pose recognition, breathing monitoring, and fall detection. It has garnered significant attention on GitHub. Emphasizing privacy and security, the project processes all data locally, supporting easy deployment via ESP32 or Docker.
This paper introduces a simulation framework for federated analysis of Multiple Sclerosis brain lesions, combining image segmentation with clinical data analysis to test federated learning methods while preserving patient privacy.
This paper introduces GCD-FGL, a federated graph learning framework designed for generalized category discovery in dynamic environments. It addresses challenges like the neighborhood absorption effect and global semantic inconsistency to improve novel category detection across distributed clients.
The article introduces OncoAgent, a dual-tier multi-agent framework designed for privacy-preserving clinical decision support in oncology. It details a system architecture that combines corrective RAG, a reflexion safety loop, and dual-tier QLoRA fine-tuning optimized for AMD hardware.
FACTS introduces an agentic workflow for query-focused table summarization that generates reusable offline templates combining SQL queries and Jinja2 templates, enabling fast, accurate, and privacy-compliant summarization without exposing sensitive data. The approach outperforms existing baselines by avoiding costly fine-tuning and token-limit issues while maintaining scalability across tables with shared schemas.
This paper evaluates LLM-based simulators as generators of differentially private synthetic data, using PersonaLedger to assess whether LLMs can faithfully reproduce statistical distributions from DP-protected personas. While achieving promising fraud detection utility (AUC 0.70 at ε=1), the study identifies significant distribution drift caused by systematic LLM biases that override input statistics.
EdgeDetect is a federated intrusion detection system for 6G-IoT environments that combines importance-aware gradient binarization (32× compression) with Paillier homomorphic encryption to achieve 98% accuracy on CIC-IDS2017 while reducing communication overhead by 96.9% and enabling deployment on resource-constrained devices like Raspberry Pi 4.