Hi Everyone,
I am trying to build a centralized federated learning, where the edge nodes have different architectures, than the master node, is it possible to design this in TensorFlow,?
In TensorFlow, building a heterogeneous federated learning system where edge nodes have different architectures than the master node is possible but challenging. You’ll need to design adaptable models and potentially extend TensorFlow Federated (TFF) to handle the architectural differences. Key considerations include model aggregation methods, communication efficiency, data heterogeneity, and resource constraints on edge nodes. Custom solutions and significant customization of TFF may be necessary to address these challenges.