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Mastering CodingBat (Ja*a), Vol. 1: Basics is now also available as an ebook for Amazon Kindle. Amazon also offers a free reader app for for further information, including a sample PDF. This entry was posted in CodingBat: Ja*a Our paper “A Performance Evaluation of Federated Learning Algorithms” has been accepted at the Second Workshop on Distributed A Performance Evaluation of Federated Learning Algorithms Adrian Nilsson, Simon Smith, Gregor Ulm, Emil Gustavsson, Mats Jirstrand (Fraunhofer-Chalmers Centre & Fraunhofer Center for Machine Learning) Federated learning proposes an environment for distributed machine learning where a global model is learned by aggregating models that have been trained locally on data generating clients. Contrary to centralized optimization, clients can be very large in number and are characterized by challenges of data and network heterogeneity. Examples of clients include smartphones and connected vehicles, which highlights the practical relevance of this approach to distributed machine learning. We compare three algorithms for federated learning and benchmark their performance against a centralized approach where data resides on the server. The algorithms covered are Federated Averaging (FedAvg), Federated Stochastic Variance Reduced Gradient, and CO-OP. They are evaluated on the MNIST dataset using both i.i.d. and non-i.i.d. partitionings of the data. Our results show that, among the three federated algorithms, FedAvg achieves the highest accuracy, regardless of how data was partitioned. Our comparison between FedAvg and centralized learning shows that they are practically equivalent when i.i.d. data is used, but the centralized approach outperforms FedAvg with non-i.i.d. data. October 2018. Zalora is hosting the event at their Singapore HQ. Their address is 298 Jalan Besar #03-01, Singapore 208959, Singapore. In order to attend, join the Meetup group and bring an ID docu*ent. There is a separate event page Introduction to OTP, Functional Federated Learning in Erlang