What is the difference between edge computing and federated learning?
Federated systems or Fog models, basically push computation from the active system side to server or network side processes. This is commonly used when computationally expensive services are required on limited systems (such as running some AI or augmentation occlusion processing from on phone), allowing for distributed data processing. Here is a good paper on the matter- https://arxiv.org/abs/1602.05629 What you might be caught up on is edge computing processes and Fog both operate the same. It's just the model differentiating where the services run.
Edge Computing is an approach for extended from cloud computing which leverages the same concept but has its advantage like mitigating the latency, resource usage, energy usage and so on.
Federated learning is just an algorithm or a kind of approach which empower the edge computing by applying the technique of model iteration instead of fetching data from the device. It also removes privacy concern in edge computing.