The advancements and progress in artificial intelligence (AI) and machine learning, and the numerous availabilities of mobile devices and Internet technologies together with the growing focus on multimedia data sources and information processing have led to the emergence of new paradigms for multimedia and edge AI information processing, particularly for urban and smart city environments. Compared to cloud information processing approaches where the data are collected and sent to a centralized server for information processing, the edge information processing paradigm distributes the tasks to multiple devices which are close to the data source. Edge information processing techniques and approaches are well suited to match current technologies for Internet of Things (IoT) and autonomous systems, although there are many challenges which remain to be addressed. Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services closer to data sources. EC combined with Deep Learning (DL) is a promising technology and is widely used in several applications.

1. Machine Learning
for Multimedia and
Edge Information
Processing
BY: MANAL ALI ALGADABY

2. Introduction
The advancements and progress in artificial intelligence (AI) and machine learning, and the
numerous availabilities of mobile devices and Internet technologies together with the growing
focus on multimedia data sources and information processing have led to the emergence of
new paradigms for multimedia and edge AI information processing, particularly for urban and
smart city environments. Compared to cloud information processing approaches where the
data are collected and sent to a centralized server for information processing, the edge
information processing paradigm distributes the tasks to multiple devices which are close to
the data source. Edge information processing techniques and approaches are well suited to
match current technologies for Internet of Things (IoT) and autonomous systems, although
there are many challenges which remain to be addressed.

3. Introduction
Edge Computing (EC) is a new architecture
that extends Cloud Computing (CC) services
closer to data sources. EC combined with
Deep Learning (DL) is a promising technology
and is widely used in several applications.

4. Problem
This research addresses the problem of efficient
scheduling of real-time multimedia tasks in edge
devices. Edge devices, such as smartphones, IoT
devices, and edge servers, have limited
computational resources and energy constraints.
Moreover, real-time multimedia tasks, such as video
streaming and image processing, require fast and
high-quality execution. Therefore, there is a need for
a smart scheduling mechanism that can distribute
resources effectively and ensure real-time
performance for multimedia tasks in edge devices.

5. Solution
The proposed solution is based
on federated learning, which
leverages the computational
capabilities of edge devices
while preserving data privacy.

6. The importance
and objectives.

7. The importance
Research and study on this topic has
contributed to the development of
new and innovative technologies to
improve computing performance at
the edges and achieve more efficient
and secure applications in areas
such as smart industry, healthcare,
smart cities, and others.

8. objectives.
Edge Cloud Computing refers to the deployment of cloud services in
strategic locations at the edge of the network infrastructure. The
main objectives and goals of Edge Cloud Computing are to reduce
data latency and maintain high availability. By bringing cloud services
closer to the data source, Edge Cloud Computing aims to improve
response time, handle high data volumes, ensure privacy, cater to
remote areas, optimize costs, and enable autonomous operations

9. objectives.
Improved data security
Edge computing reduces the amount of data
transmitted and processed in the cloud,
keeping sensitive data on user devices and
reducing the risk of data compromise
Reduced latency:
By processing data closer to the source, edge
computing minimizes the time it takes for
data to travel to the cloud and back, enabling
real-time or near-real-time applications
Enhanced reliability
With edge computing, applications can
continue to function even if there is a
disruption in the network connection to the
cloud, ensuring uninterrupted service
Scalability
Edge computing allows for the deployment
of compute and storage resources at
multiple edge locations, enabling scalability
and supporting the growing number of
connected devices and data sources
Bandwidth optimization
Edge computing reduces the amount of data
that needs to be transmitted over the
network, optimizing bandwidth usage and
reducing congestion
Cost Reduction
Edge Computing reduces data transmission
costs by processing data at the edge, closer
to the source, thereby minimizing bandwidth
usage and saving on communication costs

10. newly
implemented
models

11. OpenNESS
• OpenNESS is an open source
framework that aims to facilitate
the development and deployment
of edge computing applications.
• OpenNESS provides application
programming interfaces (APIs)
and tools to make it easier to
develop and manage applications
on edge computing infrastructure.
• OpenNESS supports many
different use cases such as
artificial intelligence, industrial
automation, cloud gaming, and
augmented reality.
Akraino MobiledgeX
Here are three models implemented in
the field of edge computing:
• Akraino is an open source project
aimed at accelerating the
development and deployment of
edge computing.
• Akraino provides a set of models,
tools and projects based on edge
computing.
• Akraino aims to achieve
compatibility and integration
between hardware, software and
services used in edge computing.
• MobiledgeX aims to provide an
edge computing infrastructure
that allows edge computing
applications to be easily
developed and deployed.
• MobiledgeX provides application
programming interfaces (APIs)
and tools to make it easier to
develop and manage applications
on edge computing infrastructure.
• MobiledgeX allows developers to
access edge computing resources
deployed in the mobile
communications network.

12. Distributed Machine Learning
The learning process is distributed across multiple
local machines in an edge network, where
machine learning models are trained on local
data and results are shared between machines..
Transfer Learning
Machine learning models are used that
are pre-trained on big data in the
remote cloud, and then modified and
improved using local data in the
edge network
Federated Learning
Machine learning models are trained on local data for
each device in the edge network, and then
important information and collected results are
shared between devices
Collaborative Learning
where knowledge and results are shared
between devices to improve model
performance and enhance
predictive ability.
performance of models
Ensemble Learning
A group of independent machine learning
models is used and their results are
collected to obtain better and more
accurate predictions.
03 04 05
02
01

13. challenges
Complexity
of the topic
The field of machine learning for
multimedia and edge information
processing is a complex and
diverse field.
It requires a deep understanding
of advanced techniques in machine
learning, multimedia processing,
and edge computing.
Lack of resources
Dealing with
big data
difficulty in obtaining the resources
necessary for research and
experiments in this field.
May need access to large data sets
and powerful computing hardware
to carry out experiments and tests.
With the increasing volume of data
related to multimedia and
information at the edges, it
becomes difficult to store and
process this data.
May need to develop techniques
and tools to handle and analyze big
data efficiently.

14. References
• Zhiyu Wang, Mohammad Goudarzi, Mingming Gong, Rajkumar Buyya,(2023) Deep Reinforcement Learning-based scheduling for
optimizing system load and response time in edge and fog computing environments,Future Generation Computer Systems,
• Cheng, X., et al. (2022). Providing Location Information at Edge Networks: A Federated Learning-Based Approach.
• Al-Rakhami, M., et al. (2023). Challenges and Techniques in Distributed Machine Learning for Edge Computing.
• Wang, Y., et al. (2023). Distributed Machine Learning Techniques for Edge Computing: A Comprehensive Survey.
• Verbraeken, R., et al. (2023). Challenges and Opportunities in Distributed Machine Learning for Edge Computing
• Zhou, Y., et al. (2023). Distributed Machine Learning in Edge Computing: Challenges and Techniques
• Dianlei Xu, et al. (2023). Distributed Machine Learning in Edge Computing: Challenges and Techniques