Federated Learning is a distributed machine learning technique that enables multiple devices or organizations to collaboratively train a model without sharing their raw data. Instead, it focuses on exchanging model updates, such as gradients or parameters, while keeping data decentralized and private.
In this approach, a central server coordinates the process by distributing an initial model to participating devices. Each device trains the model on its local dataset and sends only the necessary updates back to the server. The server then aggregates these updates—often using methods like Federated Averaging—to refine the global model, which is redistributed for further iterations.
Key benefits include enhanced data privacy, as sensitive information remains on local devices; reduced data transfer costs, making it efficient for edge computing; and the ability to leverage diverse, real-world data sources without centralization.
Common applications span healthcare, where hospitals can train models on patient data without sharing it; finance, for fraud detection across institutions; and IoT, for improving device performance through collective learning.
Challenges involve managing communication inefficiencies, handling non-independent and identically distributed (non-IID) data across devices, ensuring security against potential attacks, and maintaining model accuracy in heterogeneous environments.
Overall, Federated Learning represents a pivotal advancement in privacy-preserving AI, fostering collaboration while adhering to data protection regulations.
Table of contents
- Part 1: Best AI quiz making software for creating a federated learning quiz
- Part 2: 20 federated learning quiz questions & answers
- Part 3: Try OnlineExamMaker AI Question Generator to create quiz questions
Part 1: Best AI quiz making software for creating a federated learning quiz
OnlineExamMaker is a powerful AI-powered assessment platform to create auto-grading federated learning assessments. It’s designed for educators, trainers, businesses, and anyone looking to generate engaging quizzes without spending hours crafting questions manually. The AI Question Generator feature allows you to input a topic or specific details, and it generates a variety of question types automatically.
Top features for assessment organizers:
● Combines AI webcam monitoring to capture cheating activities during online exam.
● Enhances assessments with interactive experience by embedding video, audio, image into quizzes and multimedia feedback.
● Once the exam ends, the exam scores, question reports, ranking and other analytics data can be exported to your device in Excel file format.
● API and SSO help trainers integrate OnlineExamMaker with Google Classroom, Microsoft Teams, CRM and more.
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Part 2: 20 federated learning quiz questions & answers
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1. What is the primary goal of federated learning?
A. To centralize data for better model training
B. To train machine learning models without sharing raw data
C. To minimize the number of devices used in training
D. To increase data transfer costs for security
Answer: B
Explanation: Federated learning allows multiple devices to collaboratively train a model while keeping data decentralized, thus preserving privacy.
2. Which of the following is a key advantage of federated learning?
A. Reduced computational power on edge devices
B. Enhanced data privacy and security
C. Elimination of model aggregation
D. Requirement for a central server to store all data
Answer: B
Explanation: By performing local training and only sharing model updates, federated learning minimizes the risk of data breaches.
3. In federated learning, what does the term “federated averaging” refer to?
A. Averaging data samples across devices
B. Aggregating model updates from clients to form a global model
C. Randomly selecting devices for training
D. Combining different algorithms into one model
Answer: B
Explanation: Federated averaging (FedAvg) is a standard algorithm that combines local model updates from participating devices to improve the global model.
4. Which challenge is commonly associated with federated learning?
A. Overfitting due to excessive data sharing
B. Communication overhead between devices and the server
C. Lack of need for encryption
D. Unlimited bandwidth availability
Answer: B
Explanation: The process of sending model updates back and forth can consume significant bandwidth, making efficiency a key issue.
5. Federated learning is particularly useful in which scenario?
A. When all data is stored in a single database
B. For applications involving sensitive user data, like mobile keyboards
C. When real-time data centralization is required
D. For training models on public datasets only
Answer: B
Explanation: It enables training on decentralized, sensitive data (e.g., user inputs on phones) without compromising privacy.
6. What role does the central server play in a typical federated learning setup?
A. It stores all raw data from clients
B. It coordinates the training process and aggregates updates
C. It performs all computations locally
D. It is optional and not used in most cases
Answer: B
Explanation: The central server manages the global model by aggregating updates from clients, but it does not access raw data.
7. How does federated learning differ from traditional centralized machine learning?
A. It requires more data sharing
B. It trains models on data distributed across devices
C. It eliminates the need for algorithms
D. It focuses solely on unsupervised learning
Answer: B
Explanation: Unlike centralized ML, federated learning keeps data on local devices and only exchanges model parameters.
8. Which technique is often used to protect data in federated learning?
A. Plain text transmission of updates
B. Differential privacy
C. Public sharing of all model weights
D. Storing data on the central server
Answer: B
Explanation: Differential privacy adds noise to updates to prevent inference attacks while maintaining model utility.
9. In federated learning, what happens during the local training phase?
A. The global model is discarded
B. Each device trains the model on its local data
C. All devices share their data with each other
D. The central server takes over training
Answer: B
Explanation: Local training allows each device to update the model based on its own data before sending updates back.
10. What is a potential drawback of federated learning in heterogeneous environments?
A. Uniform data distribution
B. Non-IID (non-independent and identically distributed) data across devices
C. Excessive data homogeneity
D. Simplified model convergence
Answer: B
Explanation: Variations in data distribution can lead to slower convergence or biased global models.
11. Which organization is commonly associated with pioneering federated learning research?
A. Google
B. Microsoft
C. Apple
D. Amazon
Answer: A
Explanation: Google’s research, such as the Federated Learning for mobile keyboards, helped popularize the concept.
12. What does “client” refer to in the context of federated learning?
A. The central server
B. The devices or edge nodes holding local data
C. The final trained model
D. The aggregation algorithm
Answer: B
Explanation: Clients are the decentralized entities that perform local training and send updates to the server.
13. How can federated learning improve efficiency in mobile applications?
A. By requiring constant internet connectivity for data upload
B. By allowing on-device training to reduce data transfer
C. By centralizing all processing on servers
D. By ignoring local device constraints
Answer: B
Explanation: On-device training minimizes the need to send large amounts of data, saving bandwidth and battery life.
14. In federated learning, what is the purpose of model quantization?
A. To increase the size of model updates
B. To reduce the communication cost by compressing updates
C. To add more layers to the model
D. To eliminate the need for aggregation
Answer: B
Explanation: Quantization lowers the precision of model parameters, making updates smaller and faster to transmit.
15. Which type of learning is federated learning most closely related to?
A. Supervised learning only
B. Distributed machine learning
C. Reinforcement learning exclusively
D. Unsupervised clustering
Answer: B
Explanation: It builds on distributed learning principles by decentralizing data while coordinating model training.
16. What could cause a federated learning system to fail?
A. High availability of devices
B. Poor synchronization of updates
C. Excessive data sharing
D. Simplified aggregation methods
Answer: B
Explanation: If updates are not properly synchronized, the global model may not converge effectively.
17. Federated learning is ideal for which industry?
A. Finance, due to the need for centralized data
B. Healthcare, for handling patient data privacy
C. Manufacturing, where all data is uniform
D. Retail, without privacy concerns
Answer: B
Explanation: It allows hospitals or devices to train models on sensitive health data without sharing it.
18. What is the typical output of a federated learning round?
A. Raw data from all clients
B. An updated global model
C. Individual device models only
D. Encrypted data packets
Answer: B
Explanation: Each round results in an improved global model through aggregation of local updates.
19. How does federated learning address the issue of data silos?
A. By merging all data into one silo
B. By enabling collaborative learning without data movement
C. By ignoring data silos altogether
D. By requiring data export to a central location
Answer: B
Explanation: It allows organizations to leverage siloed data for training while keeping it in place.
20. What is a common evaluation metric in federated learning experiments?
A. Total data size
B. Accuracy of the global model
C. Number of central servers
D. Data transfer speed
Answer: B
Explanation: The global model’s performance, such as accuracy, is key to assessing the effectiveness of the federated approach.
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Part 3: Try OnlineExamMaker AI Question Generator to create quiz questions
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