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Federated Learning (FL) enables the edge devices to collaboratively train a joint model without sharing their local data. This decentralised and distributed approach improves user privacy, security, and trust. Different variants of FL algorithms have presented promising results on both IID and skewed Non-IID data. However, the performance of FL algorithms is found to be sensitive to the FL system parameters and hyperparameters of the used model. In practice, tuning the right set of parameter settings for an FL algorithm is an expensive task. In this preregister paper, we propose an empirical investigation on five prominent FL algorithms to discover the relation between the FL System Parameters (FLSPs) and their performance. The FLSPs adds extra complexity to FL algorithms over a traditional ML system. We hypothesise that choosing the best FL algorithm for the given FLSP is not a trivial problem. Further, we endeavour to formulate a single easy-to-use metric which can describe the performance of an FL algorithm, thereby making the comparison simpler.

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