Computational Sustainability

 Integrating computational theory and information theory into Advaita Vedanta, an ancient Indian philosophical system, involves reinterpreting core concepts—such as Brahman (the ultimate reality), Atman (individual self), and Maya (illusion)—through a lens of data, algorithms, and the mechanics of information processing. This modern reframe can offer insights into how consciousness, reality, and self-identity might be understood as elements within a grand computational framework. Here’s a structured approach: 1. Brahman as the Universal Data Field In Advaita Vedanta, Brahman is the singular, indivisible reality that underlies all existence. To reframe this with computational theory: Universal Data Field : Brahman could be envisioned as a boundless information field encompassing all possible states and data configurations. It is a shared pool of "all information" from which every experience, form, and process emerges. Data as Pure Potential : This field doesn’t operate on trad...

  Federated Learning (FL) Overview: Federated Learning is a decentralized machine learning approach where models are trained locally on distributed devices or sensor nodes without centralizing the data. Instead of sending raw data to a central server, each node trains a model independently, and only the trained model (or its updates) is sent back to a central server for aggregation. This allows for a collaborative global model that leverages insights from multiple nodes, while the raw sensor data remains localized, ensuring data privacy. Key Advantages for Environmental Sensing Networks: Data Privacy : Environmental data, especially when collected in sensitive areas or by private organizations, may be subject to privacy regulations or concerns. By keeping the data on the local node and only transmitting model updates, FL ensures that sensitive information is not exposed during training. Bandwidth Efficiency : Transmitting large amounts of raw sensor data over networks, particularl...