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1. Bangalore Institute of Technology
K.R. Road, V.V. Pura, Bengaluru.-560004.
Department of Information Science &
Engineering
Presented By Under the Guidance of
1BI20IS052 Pavithra N
Manoj B Kulkarni Dept of ISE , BIT
“Visual Object Detection for Privacy-Preserving
Federated Learning”

2. • Introduction
• Literature Survey
• Proposed System
• Applications
• References
Agenda

3. INTRODUCTION
• The challenge of building visual object detection models on large training datasets
due to privacy concerns and difficulties in collecting and sharing data across
organizations.
• The use of Federated Learning (FL) as a distributed machine learning paradigm that
allows participants to train local models while ensuring data privacy and security.
• The proposal of FedVisionBC, a blockchain-based federated learning system for
visual object detection, to overcome single point of failure, model poisoning attacks,
and membership inference attacks in traditional federated learning

4. K.R. Road, V.V. Pura, Bengaluru.-560004.
Department of Computer Science & Engineering
1. Title: YOLOv3: An Incremental Improvement
• Authors: Joseph Redmon, Ali Farhadi
• Year: 2019 (arXiv)
• Proposed Idea: introduce updates and improvements to the YOLOv3 object
detection algorithm. These enhancements include changes to the bounding box
prediction method, the introduction of a new network architecture called Darknet-53,
and comparisons with other detection methods
LITERATURE SURVEY

5. K.R. Road, V.V. Pura, Bengaluru.-560004.
Department of Computer Science & Engineering
• Methodology
YOLOv3 object detection algorithm involves making design changes, training a
new network, using anchor boxes for bounding box prediction, and implementing a
feature extractor network called Darknet-53
• Limitations
• May not perform well real-world scenarios
• Performs well only in COCO dataset
• It doesn’t completely overcome the limitations of YOLOv3

6. K.R. Road, V.V. Pura, Bengaluru.-560004.
Department of Computer Science & Engineering
2. Title: Federated Machine Learning: Concept and Applications
• Authors: Qiang Yang, Yang Liu, Tianjian Chen, Yongxin Tong
• Year: 2020 (arXiv)
• Proposed Idea: Federated Machine Learning enables training models across
decentralized devices while keeping data local, preserving privacy. Applications
span healthcare for secure patient data analysis, energy sector optimization in smart
grids, and improving user experience in mobile devices without compromising
privacy.

7. K.R. Road, V.V. Pura, Bengaluru.-560004.
Department of Computer Science & Engineering
Methodology:-
• Vertical Federated Learning System: This methodology involves collaborating on a model where each
party holds different features of the data. For example, one party might have information about
customer demographics while another has purchasing history.
• Horizontal Federated Learning System: Here, multiple parties possess similar data types but different
samples. For instance, different hospitals might have patient records for different regions. Horizontal
federated learning enables training a model across these datasets without centralizing the data,
preserving data locality and privacy.
• Federated Transfer Learning: This approach combines transfer learning with federated learning. A
pre-trained model on one dataset (source domain) is fine-tuned on another dataset distributed across
multiple parties (target domain), leveraging knowledge from the source domain to improve learning
efficiency and performance in the target domain while respecting data privacy.

8. Architecture
Limitations:-
• Data Fragment: Refers to the challenge of data being distributed across multiple parties, leading to fragmentation.
• Heterogeneous Data: Describes the scenario where data across different parties vary significantly in terms of
format, structure, or semantics.
• Security and Privacy Concerns: In federated learning, ensuring the security and privacy of data is paramount.
Techniques such as encryption, differential privacy, and secure aggregation are employed to protect sensitive
information and prevent unauthorized access or leakage during model training and inference.

9. K.R. Road, V.V. Pura, Bengaluru.-560004.
Department of Computer Science & Engineering
3. Title: Blockchain and Federated Learning for Privacy-Preserved Data Sharing in
Industrial IoT
• Authors: Yunlong Lu, Yueyue Dai, Yan Zhang
• Year: June 2021 (IEEE)
• Proposed Idea: Blockchain and federated learning to create a secure data sharing
architecture that maintains privacy by sharing data models instead of actual data,
achieving high accuracy, efficiency, and security for distributed multiple parties.
• Methodology
It consists of two modules: a permissioned blockchain module and a federated learning
module. The permissioned blockchain establishes secure connections among all end
IoT devices, while the federated learning module maintains data privacy by sharing the
data model instead of the actual data.

10. Architecture
Limitations:
• May not be suitable for all types of data sharing scenarios.

11. K.R. Road, V.V. Pura, Bengaluru.-560004.
Department of Computer Science & Engineering
4. Title: Contour-Aware Recurrent Cross Constraint Network for Salient Object
Detection
• Authors: Cuili Yao, Yuqiu Kong, Lin Feng, Bo Jin
• Year: Dec 2022 (IEEE)
• Proposed Idea: The Contour-Aware Recurrent Cross Constraint Network (CARCCNet),
which is a fully convolutional neural network designed to improve object contour
detection in salient object detection tasks. The network incorporates recurrent cross
constraint modules to enhance the detection of object contours.

12. K.R. Road, V.V. Pura, Bengaluru.-560004.
Department of Computer Science & Engineering
• Methodology
It involves training and evaluating the proposed CARCCNet on five popular salient
object detection datasets, describing the network architecture and each module,
conducting ablation study experiments, and presenting implementation and
evaluation metrics results compared with other state-of-the-art SOD methods.
• Limitations
• It doesn’t show same performance for different salient object data set
• Computational complexity of real-time applications

13. 5. Title: Blockchain-Federated-Learning and Deep Learning Models for COVID-19
Detection Using CT Imaging.
• Authors: Rajesh Kumar, Abdullah Aman Khan, Jay Kumar.
• Year: July 2022 (IEEE)
• Proposed Idea: is a device-aware DNN-based object detection offloading
framework for mobile edge devices. It aims to optimize resource utilization by
considering factors such as computing power and network bandwidth, and uses a
greedy algorithm to find the optimal offloading decision in polynomial time.

14. Architecture

15. Methodology:
• Problem Formulation: This step involves precisely defining the task or objective of the study, including
identifying the problem to be solved, specifying the input and output variables, and delineating any
constraints or requirements.
• Greedy Algorithm: A heuristic approach that makes locally optimal choices at each step with the hope of
finding a global optimum. Greedy algorithms are often used when solving optimization problems and can
offer simplicity and efficiency, but they may not always guarantee the best solution.
• Experimental Validation: This phase involves designing and conducting experiments to assess the
performance and effectiveness of the proposed methodology or algorithm. Experimental validation
provides empirical evidence to support the claims and conclusions drawn from the study.

16. K.R. Road, V.V. Pura, Bengaluru.-560004.
Department of Computer Science & Engineering
PROPOSED SYSTEM
• The proposed system is called FedVisionBC.
• It uses an aggregation node and a verification node instead of a central server to
solve the single point of failure problem.
• It also employs encryption techniques, verification nodes, and smart contracts to
resist model poisoning attacks.
• The system integrates the Ethereum blockchain, federated learning, differential
privacy technology, smart contracts, and Interplanetary File System (IPFS).
• It uses the ADPFedAvg algorithm to prevent membership inference attacks using
user-level differential privacy technology and the federated average algorithm.

17. Architecture
Training process of FedVisionBC
Framework of FedVisionBC

18. ALGORITHMS
1. FedAvg (Federated Average Algorithm):
• FedAvg is a key algorithm in federated learning. It facilitates participants to train local models on their
data while preserving data privacy and security.
• It encompasses stages such as initialization, local model training, validation, aggregation, and update,
ensuring a collaborative approach to model development.
• It mitigates privacy risks by separating model training from direct access to raw training data.
• This decoupling ensures that sensitive information is not exposed during the learning process.

19. 2. ADPFedAvg (Adaptive Differential Privacy Federated Learning Algorithm), :
• ADPFedAvg introduces user-level differential privacy technology and combines it with adaptive
clipping technology
• The algorithm augments global model updates, obtained by averaging local updates, with Gaussian
noise. This addition contributes to the privacy protection of the global model while maintaining its
utility.

21. Methodology
•Blockchain Integration: The system integrates blockchain technology to address the single point of failure
in traditional federated learning, ensuring a decentralized and secure environment for model aggregation and
updates.
•Privacy Protection: The methodology focuses on safeguarding user privacy by implementing encryption,
digital signature technology, and verification nodes to reduce the risk of poisoning attacks in the federated
learning process.
•Smart Contracts: A smart contract is designed within the framework to automate protocol execution and
manage interactions between nodes involved in the federated object detection system, ensuring secure and
efficient operations.
•Intelligent Aggregation: An intelligent aggregation method is employed to evaluate and select optimal
models without leaving operational traces, enhancing the efficiency and effectiveness of model selection in
the federated learning process.

22. RESULTS

23. APPLICATIONS
• Surveillance Systems
• Autonomous Vehicles
• Traffic Management
• Industrial Automation
• Robotics
• Security Systems

24. K.R. Road, V.V. Pura, Bengaluru.-560004.
Department of Computer Science & Engineering
REFERENCES
[1] J. Zhang, J. Zhou, J. Guo and X. Sun, "Visual Object Detection for Privacy-Preserving Federated Learning," in IEEE Access, vol.
11, pp. 33324-33335, 2023
[2] Joseph Redmon, Ali Farhadi "YOLOv3: An Incremental Improvement" arXiv:1804.02767v1 [cs.CV] 8 Apr 2019.
[3] Qiang Yang,Yang Liu, Tianjian Chen ,Yongxin Tong “Federated Machine Learning:Concept and Applications”
arXiv:1902.04885v1 [cs.AI] 13 Feb 2020
[4] Yunlong Lu, Yueyue Dai, Yan Zhang “Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT”
IEEE Transactions on Industrial Informatics, Vol. 16, No. 6, June 2021
[5] Cuili Yao, Yuqiu Kong, Lin Feng, Bo Jin “Contour-Aware Recurrent Cross Constraint Network for Salient Object Detection” IEEE
Access, December 16, 2022.
[6] Rajesh Kumar, Abdullah Aman Khan, Jay Kumar “Blockchain-Federated-Learning and Deep Learning Models for COVID-19
Detection Using CT Imaging” IEEE Sensors Journal, Vol. 21, No. 14, July 15, 2022

25. K.R. Road, V.V. Pura, Bengaluru.-560004.
Department of Computer Science & Engineering
THANK YOU