AIBullisharXiv – CS AI · May 127/10
🧠FairHealth is an open-source Python library designed to address critical gaps in healthcare AI for low-resource settings, particularly in low-income countries. The toolkit integrates fairness auditing, privacy-preserving federated learning, explainability tools, and Global South datasets into a unified framework, making trustworthy AI more accessible to underserved healthcare systems.
AIBullisharXiv – CS AI · May 127/10
🧠Researchers have developed M2AE, a cross-modal foundation model trained on 3.4 million paired ECG and PPG signals that creates compact 'biosignal fingerprints' for cardiovascular monitoring. These privacy-preserving representations enable accurate disease detection and risk prediction across multiple clinical tasks while functioning with single-sensor wearables, addressing the scalability gap between diagnostic-grade ECG and ubiquitous PPG sensors.
AIBearisharXiv – CS AI · Apr 207/10
🧠Researchers identify a critical vulnerability in federated learning systems where malicious 'dictator clients' can erase other participants' contributions while preserving their own, compromising the collaborative training process. The study provides theoretical and empirical analysis of single and multiple dictator scenarios, revealing fundamental security weaknesses in decentralized machine learning architectures.
AINeutralarXiv – CS AI · 4d ago6/10
🧠Researchers introduce an anonymous gradient-boosted decision tree (GBDT) protocol enabling secure training on vertically partitioned data between two parties while hiding record identifiers. The approach uses dual circuit-PSI and oblivious pseudorandom functions to eliminate ID exposure risks inherent in standard private set intersection methods, while achieving computational efficiency comparable to non-private approaches.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduce UMEDA, a federated learning framework designed to enable device-free localization across heterogeneous sensors while maintaining privacy. The system uses spectral signal processing and diffusion-based aggregation to align data from different sensor modalities without requiring direct node correspondence, achieving superior performance on multi-modal benchmarks under privacy constraints.
AINeutralarXiv – CS AI · May 116/10
🧠Researchers present the first theoretical framework for differentially private reinforcement learning with general function approximation, achieving regret bounds of Õ(K^3/5) that match linear-case performance. This breakthrough extends privacy guarantees beyond tabular and linear settings, combining batched policy updates with the exponential mechanism for improved privacy-utility tradeoffs in online RL systems.
AINeutralarXiv – CS AI · May 116/10
🧠Researchers present a federated learning approach to detect passive eavesdropping attacks in smart grids by combining graph neural networks with temporal modeling. The system achieves 98.32% per-timestep accuracy while preserving data privacy through decentralized training, addressing a critical vulnerability in grid infrastructure where attackers silently gather topology and consumption data.
AIBullisharXiv – CS AI · May 96/10
🧠PACZero introduces a novel PAC-private fine-tuning mechanism for large language models that achieves usable utility while maintaining zero mutual information leakage, surpassing traditional differential privacy approaches. Using sign quantization of zeroth-order gradients, the method exploits moments of unanimous agreement across candidate subsets to eliminate privacy costs, demonstrating competitive performance on benchmark tasks like SST-2 and SQuAD.
AINeutralarXiv – CS AI · Apr 146/10
🧠Researchers propose FedRio, a federated learning framework that enables social media platforms to collaboratively detect bot accounts without sharing raw user data. The system uses graph neural networks, adversarial learning, and reinforcement learning to improve bot detection accuracy while maintaining privacy across heterogeneous platform architectures.
