Functional Federated Learning in Erlang (ffl-erl)
By Gregor Ulm, Emil Gustavsson, Mats Jirstrand. 2018
21 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jakub Konecný
- Virginia Smith
- Ameet Talwalkar
- Tian Li
- Peter Richtárik
- H. Brendan McMahan
- Sebastian Caldas
- Aryan Mokhtari
In Computer Networks and Communications
- Tian Li
- Ameet Talwalkar
- Virginia Smith
- Sebastian Caldas
- H. Brendan McMahan
- Jakub Konecný
- Elad Hazan
- Shusen Wang
In Information Systems
- Jakub Konecný
- H. Brendan McMahan
- Virginia Smith
- Ameet Talwalkar
- Peter Richtárik
- Zachary B. Charles
- Dusist Niyato
Citations in the corpus
- LEAF: A Benchmark for Federated Settings (2018, background): “…and yet do not provide a realistic model of a federated scenario, e.g., artificial partitions of MNIST, MNIST-fashion or CIFAR-10 [22, 13, 8, 3, 11, 32, 34]; (2) realistic but proprietary federated datasets, e.g., data from an unnamed social network in [22], crowdsourced voice commands in [18],…”