Category Archives: Papers

Accepted Paper at DIDL 2018

Our paper “A Performance Evaluation of Federated Learning Algorithms” has been accepted at the Second Workshop on Distributed Infrastructures for Deep Learning (DIDL 2018), which is colocated with the 2018 ACM/IFIP International Middleware Conference (Middleware 2018). This conference will take place from December 10 to 14 in Rennes, France. The abstract is reproduced below.

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.

Preprint of “Functional Federated Learning in Erlang (ffl-erl)” available on arXiv

Our paper “Functional Federated Learning in Erlang (ffl-erl)” is now available as a pre-print on arXiv at The full title as well as the abstract are below.

Functional Federated Learning in Erlang (ffl-erl)
Gregor Ulm, Emil Gustavsson and Mats Jirstrand

The functional programming language Erlang is well-suited for concurrent and distributed applications. Numerical computing, however, is not seen as one of its strengths. The recent introduction of Federated Learning, a concept according to which client devices are leveraged for decentralized machine learning tasks, while a central server updates and distributes a global model, provided the motivation for exploring how well Erlang is suited to that problem. We present ffl-erl, a framework for Federated Learning, written in Erlang, and explore how well it performs in two scenarios: one in which the entire system has been written in Erlang, and another in which Erlang is relegated to coordinating client processes that rely on performing numerical computations in the programming language C. There is a concurrent as well as a distributed implementation of each case. Erlang incurs a performance penalty, but for certain use cases this may not be detrimental, considering the trade-off between conciseness of the language and speed of development (Erlang) versus performance (C). Thus, Erlang may be a viable alternative to C for some practical machine learning tasks.