GreedyReliabFL: Strengthening Federated Learning with Jaccard Greedy Selection and Blockchain Security

Client Selection

1. Strategy: Jaccard Greedy Selection Strategy.
   • Advancement: Paradigm-shifting approach in federated learning.
   • Key Features: Mathematical rigor, algorithmic efficiency, robust security measures.
   • Mechanism: Uses Jaccard similarity for client prioritization.
   • Benefits: Enhances diversity and representativeness of participants, ensures integrity and reliability of FL models across distributed datasets.

Enhancing Reliability in Federated Learning

1. Challenge: Ensuring reliability of participants in federated learning (FL).
2. Solution: Reputation-based selection scheme.
   • Techniques: Uses steganography to ensure integrity.
   • Factors Considered: Device characteristics (computational power, memory, energy), historical performance (accuracy, consistency).
   • Security: Incorporates verifiable random functions (VRFs) to conceal identities and enhance security.
3. Outcome: Enhances security and integrity of the selection process, improving overall FL reliability and effectiveness.

Addressing Attacks in Federated Learning

1. Challenge: Security threats from malicious participants.
2. Solution: Novel approach to identify and mitigate malicious behavior.
   • Technique: Analyze gradient differences before and after training.
   • Dimensionality Reduction: Uses Principal Component Analysis (PCA) to simplify gradient data.
   • Clustering: Groups similar updates to identify suspicious behavior.
3. Outcome: Effectively isolates malicious participants, enhancing security and reliability of FL.

Defense System Against Attacks in Federated Learning

1. Challenge: Compromised end nodes and poisoning attacks.
2. Solution: Aggregation strategy with penalization mechanism.
   • Penalization: Regularization term in local models to penalize deviations.
   • Objective: Minimize strength of attacks, encourage convergence towards a common objective.
3. Outcome: Detects and mitigates poisoning attacks, enhances robustness and security of FL.

Reward-Penalty Scheme

1. Purpose: Promote fairness and incentivize honest behavior.
   • Mechanism: Rewards for honest behavior, penalties for malicious activities.
2. Outcome: Fosters a collaborative and equitable learning environment.

Blockchain-Based Processes

1. Initialization
   • Action: Workers and task publishers register blockchain accounts and generate unique wallet addresses.
   • Benefit: Facilitates secure and transparent interactions within the decentralized network.
2. Model Retrieval
   • Action: Task publisher searches blockchain for pre-trained models; if none, initiates a new FL training task via smart contract.
   • Benefit: Efficient model management and transaction handling.
3. Launching FL Tasks Request
   • Action: Task publisher broadcasts smart contract with FL task requirements.
   • Details: Includes task ID, data types, attributes, size, selection time, total points, and rewards.
   • Worker Response: Interested workers send data type and attribute details.
4. Worker Selection
   • Action: Task publisher identifies and assesses candidates based on reputation, skill, experience, and availability.
   • Process: Two steps - pre-selection based on reputation and final selection with deposit points locking.
5. Deposit
   • Action: Establish network shard and require participants to contribute deposit points.
   • Purpose: Ensures commitment and preparation for the training process.

Experimental Validation

1. Objective: Evaluate performance of proposed mechanisms.
2. Outcome: Demonstrates effectiveness in enhancing reliability, security, and fairness in FL.

Summary

• Key Concepts: Client selection, reliability, security, penalization, reward-penalty, blockchain-based processes, experimental validation.
• Techniques Used: Jaccard similarity, steganography, VRFs, PCA, clustering, regularization, smart contracts.
• Outcomes: Improved reliability, security, robustness, and fairness in FL.

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