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The equation $$\hat{y} = \sigma(xW_\color{green}{1})W_\color{blue}{2} \tag{1}\label{1}$$ is the equation of the forward pass of a single-hidden layer fully connected and feedforward neural network, i.e. a neural network with 3 layers, 1 input layer, 1 hidden layer, and 1 output layer, where the input layer is connected to the hidden layer (all scalar inputs ...


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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 ...


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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 ...


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There are some works that do this comparison. Briefly, it's been observed that the performance of models trained via FL drops as data distributions between participating agents differ. When data is IID-like though, performance is comparable to centralized training. Some works that I'm aware of are as follows: Overcoming Forgetting in Federated Learning on ...


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The analogy is to a federal system of government. In a federation, smaller pieces follow the direction of a higher piece. In federated machine learning, you give your data for processing to the higher machine. The federation in this analogy is a collection of smaller computers. The central computer breaks up your data and gives portions of it to each smaller ...


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