Federated Computing: Decentralized Data Processing and Collaborative Machine Learning

Główne pojęcia

Federated Computing enables collaborative data processing and machine learning across distributed devices without compromising individual data privacy.

Streszczenie

The content provides a comprehensive survey on Federated Computing (FC), which is a decentralized approach to data processing and machine learning. FC addresses the challenges of traditional centralized computing models, such as data security breaches and regulatory hurdles, by enabling collaborative processing without compromising individual data privacy.
The key aspects covered in the survey are:

Definition and Distinction of Federated Learning (FL) and Federated Analytics (FA):

FL focuses on collaborative machine learning model training, while FA leverages statistical operations on distributed data.
Both fall under the broader umbrella of FC, which aims to extract insights from distributed data sources without disclosing raw data.

FC System Components and Extensions:

Basic building blocks: Client selection, aggregation strategy, and communication protocol.
Extensions: Privacy-enhancing techniques (e.g., differential privacy, secure multi-party computation) and compression methods to optimize network utilization.

FC System Architectures:

Centralized, hierarchical, and peer-to-peer architectures with varying levels of decentralization.

FC Scenarios and Challenges:

Model-centric vs. data-centric, horizontal vs. vertical, and cross-device vs. cross-silo scenarios.
Key challenges include improving insights/ML performance, enhancing privacy/security, and optimizing hardware/network utilization.

The survey provides a comprehensive taxonomy and framework to describe FC systems, highlighting the interplay between the basic building blocks and optional extensions. It also identifies research gaps and prevalent system configurations in the current literature.

Statystyki

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Cytaty

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Kluczowe wnioski z

Federated Computing -- Survey on Building Blocks, Extensions and Systems

by René... o arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02779.pdf

Głębsze pytania

How can Federated Computing be extended to support real-time, low-latency applications, such as autonomous vehicles or industrial IoT

To support real-time, low-latency applications like autonomous vehicles or industrial IoT, Federated Computing can be extended in several ways:

Edge Computing Integration: By incorporating edge computing capabilities into the FC framework, data processing and analysis can be performed closer to the data source. This reduces latency by minimizing the distance data needs to travel for processing.

Dynamic Client Selection: Implementing dynamic client selection algorithms that prioritize clients with the lowest latency connections or the most up-to-date data can ensure that real-time applications receive timely and relevant information.

Adaptive Aggregation Strategies: Developing adaptive aggregation strategies that can adjust the frequency of model updates based on the urgency of the application's requirements. For real-time applications, more frequent aggregation may be necessary to maintain low latency.

Efficient Communication Protocols: Utilizing efficient communication protocols that prioritize low latency and real-time data transmission. Protocols like WebSockets or MQTT can be optimized for quick data exchange between clients and the server.

Model Compression Techniques: Implementing model compression techniques like quantization, pruning, or knowledge distillation to reduce the size of models exchanged between clients and the server, thereby decreasing communication overhead and latency.

What are the potential societal and economic implications of widespread adoption of Federated Computing, particularly in terms of data ownership, privacy, and the balance of power between individuals and large organizations

The widespread adoption of Federated Computing can have significant societal and economic implications:

Data Ownership: FC empowers individuals to retain ownership and control over their data, shifting the balance of power from large organizations to data owners. This can lead to increased data privacy and security for individuals.

Privacy Concerns: With FC, sensitive data remains on the client devices, reducing the risk of data breaches and unauthorized access. This can enhance privacy protection for individuals and build trust in data sharing practices.

Economic Impact: The adoption of FC can lead to new business models and opportunities, especially in industries where data sharing is crucial. Companies can leverage FC to collaborate on data-driven projects while maintaining data sovereignty and compliance with regulations.

Regulatory Compliance: The rise of FC may necessitate the development of new regulations and standards to govern data sharing practices, ensuring that data privacy and security are maintained across federated systems.

Ethical Considerations: As FC aligns with principles of responsible AI and ethical data practices, its adoption can promote ethical data handling and decision-making, fostering a more transparent and accountable data ecosystem.

Given the diverse range of Federated Computing scenarios and challenges, how can a unified framework be developed to guide the design and deployment of FC systems for different use cases and environments

To develop a unified framework for guiding the design and deployment of FC systems across different scenarios, the following steps can be taken:

Standardized Taxonomy: Establish a standardized taxonomy that categorizes FC systems based on their components, extensions, and use cases. This taxonomy should provide a common language for describing and comparing FC systems.

Modular Architecture: Design FC systems with a modular architecture that allows for flexibility and scalability across diverse use cases. Each module should address specific challenges or requirements, enabling easy customization for different scenarios.

Best Practices and Guidelines: Develop best practices and guidelines for designing and deploying FC systems, considering factors like client selection, aggregation strategies, communication protocols, and privacy-enhancing techniques. These guidelines can serve as a reference for practitioners and researchers.

Benchmarking and Evaluation: Establish benchmarking criteria and evaluation metrics to assess the performance and effectiveness of FC systems in various environments. This will help in comparing different FC implementations and identifying areas for improvement.

Collaborative Research: Encourage collaborative research efforts among academia, industry, and regulatory bodies to address the complex challenges of FC deployment. By fostering collaboration, a unified framework can evolve to meet the evolving needs of diverse use cases and environments.