Resource Constraint

Resource constraints in machine learning encompass the limitations in computational power, memory, communication bandwidth, and data availability that hinder the development and deployment of sophisticated models, particularly on edge devices. Current research focuses on developing efficient algorithms and model architectures, such as quantization techniques, federated learning with optimized communication strategies, and model splitting, to overcome these limitations. This research is crucial for enabling the application of advanced machine learning techniques in resource-constrained environments, improving the efficiency and scalability of various applications from mobile computing to large-scale distributed systems. Addressing resource constraints is essential for broadening access to and expanding the capabilities of machine learning across diverse settings.