Toward TinyDPFL systems for real-time cardiac healthcare: Trends, challenges, and system-level perspectives on AI algorithms, hardware, and edge intelligence

Despite rapid advances in medical technology, cardiac diseases remain the leading cause of global mortality, with arrhythmias that pose significant diagnostic and treatment challenges. This survey presents a comprehensive review of 176 state-of-the-art contributions in machine learning (ML), federated learning (FL), TinyML, and hardware acceleration for efficient, real-time, and privacy-preserving cardiac diagnosis and care. Explores both software and hardware advancements, including differential privacy (DP), quantized neural networks, and FPGA (Field Programmable Gate Array)-based implementations optimized for edge devices and wearable devices. Key challenges, such as latency, energy constraints, adversarial robustness, and personalization, are systematically examined. The survey synthesizes solutions across algorithmic innovations, secure and adaptive FL frameworks, and neuromorphic and sparse architectures, especially FPGA-based solutions, for resource-aware inference and training. Informed by original research, it highlights emerging directions: AI-driven data mining, DP for quantized models, continual learning (CL) on the edge, FPGA-accelerators including quantized DNN, SNN, and Sparse architectures, tuneable/reconfigurable FPGA-based TinyDPFL, Multimodal heterogeneous FL, real-time adversarial detection via model watermarking. This work offers a unified system-level perspective bridging ML algorithms and edge AI hardware, guiding the development of scalable, adaptive, and trustworthy cardiac healthcare systems. Beyond surveying existing literature, it proposes forward-looking design principles to advance intelligent, secure, and practical digital cardiology.