Federated Learning

Federated Learning is a distributed machine learning approach where a model is trained collaboratively across multiple participants, each holding their own local dataset, without requiring the data to be transferred to a central location. Instead of sharing raw data, each participant trains a local model on their data and shares only the model updates (gradients or weight changes) with a central server, which aggregates them to improve the global model. This process iterates until the global model converges, achieving performance comparable to centralized training while keeping sensitive data private and local. Federated Learning for Geospatial DataGeospatial DataGeospatial data encompasses information about the location, shape, and relationships of physical features on Earth. I... PrivacyGeospatial data often carries significant privacy and sovereignty concerns that make centralized training impractical. Location data from mobile devices reveals sensitive information about individuals' movements and behaviors. Government agencies in different countries may be prohibited from sharing spatial data across borders. Commercial satellite imagerySatellite ImagerySatellite imagery consists of photographs and data captured by Earth observation satellites orbiting the planet. Thes... providers may want to collaborate on models without sharing proprietary data. Federated learning enables these parties to jointly train powerful geospatial AI models, such as land cover classifiers or mobility prediction models, while respecting data privacy regulations like GDPR and organizational data policies. Challenges and Emerging SolutionsFederated learning faces challenges including non-IID (non-independently and identically distributed) data, where participants' local datasets have different characteristics, which is natural in geospatial contexts where different regions have different land cover distributions. Communication overhead from frequent model update exchange can be substantial. Heterogeneous computing environments across participants may create bottlenecks. Federated learning is also vulnerable to adversarial participants who send malicious updates. Advances in differential privacy, secure aggregation, and communication-efficient protocols are addressing these challenges, making federated geospatial AI increasingly practical.