Answers.Body: <p>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-
  <a href="https://arxiv.org/abs/1602.05629" rel="nofollow noreferrer">https://arxiv.org/abs/1602.05629</a>
  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.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-04-25T16:54:18.113Z
Answers.LastActivityDate: 2017-04-25T16:54:18.113Z
Answers.UserDisplayName: rfdpro
Answers.Body: <p>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. </p>
  
  <p>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.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-09-24T07:24:38.653Z
Answers.LastActivityDate: 2019-09-24T13:05:27.047Z
Answers.LastEditDate: 2019-09-24T13:05:27.047Z
Answers.UserDisplayName: Najib jamshidi
Body: <p>I recently read about <a href="https://ai.googleblog.com/2017/04/federated-learning-collaborative.html" rel="nofollow noreferrer">federated learning</a> introduced by Google, but it seems to be like <a href="https://en.wikipedia.org/wiki/Edge_computing" rel="nofollow noreferrer">edge computing</a>.</p>
  
  <p>What is the difference between edge computing and federated learning?</p>
  
Tags: <machine-learning><comparison><federated-learning>
Title: What is the difference between edge computing and federated learning?
UserDisplayName: Karan Nadagoudar
ThreadText: # What is the difference between edge computing and federated learning?
  
  (Asked by: username_0 on 2017-04-17)
  
  I recently read about [federated learning](https://ai.googleblog.com/2017/04/federated-learning-collaborative.html) introduced by Google, but it seems to be like [edge computing](https://en.wikipedia.org/wiki/Edge_computing).
  
  What is the difference between edge computing and federated learning?
  
  **Tags:** machine-learning, comparison, federated-learning
  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2017-04-25. Score: 3
  
  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.
  
  
  ---
  
  ### Answer by username_2 on 2019-09-24. Score: 3
  
  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.
  
  
  ---
  
  