I disagree, federated learning is a very valid approach. The cars have a ton of computational power especially with HW3. (Although I think they don't have high precision floating point - the chips are for inference)

Federated learning is a lot like distributed training of a neural network.

The trouble with distributed training is that it's not as fast to converge as simply running more training steps. It is basically like increasing the batch size.

Also, it sounds like a general hassle for Tesla to use federated learning. I believe they need to be carefully auditing and labelling their training data for almost all their tasks. Perhaps some like depth-estimation don't require labelled data.