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draft-distributed-ml-iot-edge-cmp-foukalas-00

Document Type Active Internet-Draft (individual)
Authors Fotis Foukalas  , Athanasios Tziouvaras 
Last updated 2021-03-30
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IoT Operations Working Group                             F. Foukalas
Internet-Draft                                           A. Tziouvaras
Intended status: Draft Standard                          March 30, 2021
Expires: September, 2019                                  

draft-distributed-ml-iot-edge-cmp-foukalas-00.txt

Status of this Memo

This Internet-Draft is submitted in full conformance with the
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This Internet-Draft will expire on September 22, 2021.

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Abstract

Next generation Internet requires decentralized and distributed 
intelligence in order to make available a new type of 
experience to serve the user's interests. Such new services
will be enabled by deploying the intelligence 
over a high volume of IoT devices in a form of distributed 
protocol. Such a  protocol will orchestrate the machine learning 
(ML) application in order to train the aggregated data available 
from the IoT devices. The training is not an easy task in such 
a distributed environment, where the amount of connected IoT 
devices will scale up and the needs for both interoperability 
and computing are high. This draft, addresses both issues 
by combining two emerging technologies known as edge AI 
and fog computing. The protocol procedures aggregate the data 
collected by the IoT devices into a fog node and apply edge AI 
for data analysis at the edge of the infrastructure. The 
analysis of the IoT requirements resulted in an end-to-end ML 
protocol specification which is presented throughout this draft.

Table of Contents

1. Introduction 2
2. Background and terminology 3
3. Edge computing architecture 4
4. Protocol stages 8
4.1. Initial configuration 8
4.2. FL training 11
4.3. Cloud update 12
5. Security Considerations 14
6. IANA Considerations 15
7. Conclusions 15
8. References 15
8.1. Normative References 15
9. Acknowledgments 16

1. Introduction

There is an evident requirement to address several challenges 
to offer robust IoT services by leveraging the integration of 
Edge computing with IoT known as IoT edge computing. The concept 
of IoT edge computing has not been specified in detail yet 
although two recent drafts described already some aspects of such 
Internet architecture. Such architecture is way more useful in case 
of distributed machine learning deployment to future Internet, 
where the edge artificial intelligence will play an important role. 
Towards this end, the proposed draft provides first the IoT edge 
computing architecture, which includes the necessary elements 
to deploy distributed machine learning. Second, three stages of 
such a distributed intelligence are described in a sort of protocol 
procedures, where the initialization, the learning and cloud updates 
were devised. Details are given for all the protocol procedures 
of the distributed machine learning for IoT edge computing. 

2. Background and terminology 

Below we list a number of terms related with the distributed 
machine learning solution:

End devices: End devices [1] are IoT devices that collect 
data while also having computing and networking capabilities.
 End devices can be any type of device that can connect to the 
Edge gateway and facilitate sensors for data collection.

Edge gateway: The Edge gateway is a server that is located to 
the Edge of the network [1]. It facilitates large computational 
and networking capabilities and coordinates the FL process. 
The Edge gateway is used to relieve the traffic from the network 
backhaul as the end devices connect to the Edge instead of the 
cloud.

Cloud: Cloud supports very large computational capabilities [1] 
and is geographically located far from the end devices. It provides 
accessibility to the Edge gateway and remains agnostic on the amount 
and type of participating end devices. As a result, the cloud does 
not have an active role in the FL training process.
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