Appendices

Glossary of Technical Terms

  • Blockchain: A decentralized, immutable ledger that records transactions across a network of computers. (Source)

  • Smart Contract: Self-executing contracts with the terms directly written into code, running on a blockchain. (Source)

  • Decentralized Application (DApp): An application that operates on a decentralized network, combining smart contracts and a frontend user interface. (Source)

  • Federated Learning: A machine learning technique where multiple entities collaboratively train a model without sharing their data, maintaining data privacy. (Source)

  • InterPlanetary File System (IPFS): A peer-to-peer protocol for storing and sharing data in a distributed file system.

  • Zero-Knowledge Proof: A cryptographic method by which one party can prove to another that a statement is true without revealing any additional information. (Source)

  • Layer-2 Solution: A secondary framework or protocol built on top of an existing blockchain to improve scalability and transaction speed. (Source)

  • Consensus Algorithm: A mechanism used in blockchain networks to achieve agreement on a single data value among distributed processes or systems.

  • Node: A participant in a blockchain network that maintains a copy of the ledger and may validate transactions. (Source)

  • Byzantine Fault Tolerance: The ability of a distributed network to reach consensus despite some nodes acting maliciously or failing. (Source)

Mathematical Models and Algorithms

  • Decentralized Multi-Level Systems: Mathematical programming models for decision-making in systems with multiple hierarchical levels. (Source)

  • Learning-to-Optimize Frameworks: Data-driven approaches that train decentralized algorithms to exploit specific problem features. (Source)

  • Federated Learning Optimization: Algorithms enabling collaborative model training across decentralized data sources while preserving data privacy. (Source)

  • Inverse Distance Aggregation (IDA): An adaptive weighting approach in federated learning that uses the distance of model parameters to minimize the effect of outliers and improve convergence rates.

References to Research and Technical Papers

  1. A Mathematical Programming Model of Decentralized Multi-Level Systems: Explores decision-making models in decentralized systems. (Source)

  2. A Mathematics-Inspired Learning-to-Optimize Framework for Decentralized Systems: Presents data-driven decentralized algorithms to enhance convergence. (Source)

  3. Blockchain-Based Federated Learning: Proposes a framework integrating blockchain and federated learning for secure and fair data sharing. (Source)

  4. Get More for Less in Decentralized Learning Systems: Introduces JWINS, a communication-efficient decentralized learning system. (Source)

  5. Federated Learning Overview: Summarizes federated learning, its algorithms, limitations, and applications. (Source)

FAQs for Users, Developers, and Researchers

Users

  • How can I participate in the Lumora network? You can participate by installing the Lumora browser extension, allowing you to share unused bandwidth and earn rewards.

  • Is my data secure when using Lumora? Yes, Lumora employs advanced encryption protocols and adheres to global data privacy regulations to ensure your data remains secure.

Developers

  • How can I contribute to Lumora's development? Access the open-source repositories on GitHub, participate in hackathons, and engage in our developer community forums.

  • Are there bounties for fixing bugs or adding features? Yes, Lumora offers bounties for various contributions. Details are available on our community portal.

Researchers

  • Can I access datasets collected by Lumora for research purposes? Yes, Lumora provides a dataset marketplace where researchers can access diverse datasets for analysis and model training.

  • How does Lumora ensure data quality and integrity? Rigorous validation mechanisms, including cryptographic hash functions and consensus algorithms, maintain data quality and integrity.

Last updated