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ML-TN-007 — AI at the edge: exploring Federated Learning solutions

942 bytes added, 09:20, 5 October 2023
→‎Conclusions and future work
Analyzing the individual algorithms, it can be seen a very similar behavior between FedAvg and FedProx, which have very similar results in terms of both local <code>training_loss</code> and server <code>validation_accuracy</code>. This is mainly due to the fact that they are very similar to each other minus a proximity term mu, in the case of FedProx, which improves the convergence ratio. The Scaffold algorithm, on the other hand, has a totally different implementation from its predecessors, which allows to dynamic adjustment of the aggregation weight of each client’s update based on their historical performance, and thus achieves better performance, especially when using unbalanced classes (α = 0.1). This can easily be seen in the server <code>validation_accuracy</code> graph.
The successful execution of this more complex use case on NVFlare, involving multiple tested algorithms and diverse data heterogeneity, further underscores the framework’s robust capabilities and suitability for a wide range of scenarios. This result confirms the versatility of NVFlare as a FL framework, making it a reliable choice for real-world scenarios that requirw the management of dealing with heterogeneous and complex data.
= Conclusions and future work =
TBD One The analysis detailed in previous chapter allows to say that NVFlare is a viable framework for building real-world Federated Learning systems that make use of Linux-powered embedded platforms as clients. Nevertheless, it is worth remembering that one important issue that was purposely not addressed , yet is . For the sake of simplicity, this work did not consider the problem of labeling of new samples. In other words, it was implicitly assumed that new samples collected by the clients are somehow labelled prior to being used for training. This is a strong assumption because implies . In reality, system architects can not overlook this fundamental issue when designing FL-based solutions. This is especially true in industrial environments where clients are often unattended devices that labeling of new samples issue* operate standalone. To date, this topic hes not been investigated thoroughly yet. For instance, [https://bdtechtalks.com/2021/08/09/what-is-federated-learning/* this article] mentions it without providing practical solutions, however. Generally speaking, it seems that for industrial applications the use of unsupervised learning?**techniques could be a promising approach (see for example [https://arxiv.org/pdf/1805.03911.pdfthis paper]). In any case, the problem of labeling new samples will have to be addressed in future works to make Federated Learning truly available for real applications in the space of Industrial IoT.
=Notes=
U0001
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