Paper “Functional Federated Learning in Erlang (ffl-erl)” accepted for publication

Our paper “Functional Federated Learning in Erlang (fflerl)” has been accepted for publication in the Proceedings of the 26th International Workshop on Functional and (Constraint) Logic Programming (WFLP 2018). These proceedings will appear as Springer Lecture Notes in Computer Science Vol. 11285.

The abstract is 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, but numerical
computing is not seen as one of its strengths. Yet, the
recent introduction of Federated Learning, which leverages
client devices for decentralized machine learning tasks,
while a central server updates and distributes a global
model, motivated us to explore how well Erlang is suited to
that problem. We present the Federated Learning framework
ffl-erl and evaluate it 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. We show that Erlang
incurs a performance penalty, but for certain use cases this
may not be detrimental, considering the trade-off between
speed of development (Erlang) versus performance (C). Thus,
Erlang may be a viable alternative to C for some practical
machine learning tasks.

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.

Title:
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)

Abstract:
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.

Upcoming Talk at the Singapore Elixir and Erlang Meetup Group

I will give a talk at the Singapore Elixir and Erlang Meetup Group on Monday, 15 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 document. There is a separate event page on Meetup.com.

Below you will find the full description of the event.

Introduction to OTP, Functional Federated Learning in Erlang

Agenda:
• 6:45 pm - 7:10 pm
Snacks - Pizzas, drinks, mingling
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• 7:10 pm
Topic: Introduction to OTP

Grzegorz is the Head Of Engineering at Kaligo.

Speaker: Grzegorz Witek
Linkedin: https://www.linkedin.com/in/grzegorzwitek/

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• 8:00 pm :
Topic: Functional Federated Learning in Erlang (ffl-erl)

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 de-centralized 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.

Speaker: Gregor Ulm
Gregor Ulm is a computer scientist, currently working as a research and development engineer in an industrial research lab in Gothenburg, Sweden.

Website: http://gregorulm.com
LinkedIn: https://www.linkedin.com/in/gregorulm/

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Thanks to the following volunteers:
Meetup Organizers, Zalora for venue & Yojee for getting us pizzas.

About Yojee: An agile startup in Singapore building logistics software utilizing Block-chain, AI and Machine Learning to optimize and manage fleets.

• What to bring
ID