Glad that more and more people start recognizing FL these days.
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???????????????? ???????????????? ????. ????????-???????????? ????. ?????????????????? ???????????????? ????. ?????????????????? ????????????????. . . Most ML models are trained independently without any interaction with other models. But real-world ML uses many powerful learning techniques that rely on model interactions. The following animation summarizes four such well-adopted and must-know training methodologies: -- ?? Find a more vivid explanation with visuals here: https://lnkd.in/gBnfFCgB. -- 1) ???????????????? ???????????????? Useful when: - The task of interest has less data. - But a related task has abundant data. This is how it works: - Train a neural network model (base model) on the related task. - Replace the last few layers on the base model with new layers. - Train the network on the task of interest, but don’t update the weights of the unreplaced layers. Training on a related task first allows the model to capture the core patterns of the task of interest. Next, it can adapt the last few layers to capture task-specific behavior. 2) ????????-???????????? Update the weights of some or all layers of the pre-trained model to adapt it to the new task. The idea may appear similar to transfer learning. But here, the whole pretrained model is typically adjusted to the new data. 3) ??????????-???????? ???????????????? (??????) A model is trained to perform multiple related tasks simultaneously. Architecture-wise, the model has: - A shared network - And task-specific branches The rationale is to share knowledge across tasks to improve generalization. In fact, we can also save computing power with MTL: - Imagine training 2 independent models on related tasks. - Now compare it to having a network with shared layers and then task-specific branches. Option 2 will typically result in: - Better generalization across all tasks. - Less memory to store model weights. - Less resource usage during training. 4) ?????????????????? ???????????????? This is a decentralized approach to ML. Here, the training data remains on the user's device. So in a way, it's like sending the model to data instead of data to model. To preserve privacy, only model updates are gathered from devices and sent to the server. The keyboard of our smartphone is a great example of this. It uses FL to learn typing patterns. This happens without transmitting sensitive keystrokes to a central server. Note: Here, the model is trained on small devices. Thus, it MUST be lightweight yet useful. Model compression techniques are prevalent in such cases. I have linked a detailed guide in the comments. -- ?? Get a Free Data Science PDF (550+ pages) with 320+ posts by subscribing to my daily newsletter today: https://lnkd.in/gzfJWHmu -- ?? Over to you: What are some other ML training methodologies that I have missed here? #machinelearning