Distributed Systems

Apache Kafka is a distributed event streaming platform used for building real-time data pipelines and applications.

A consistency model defines the behavior of data in distributed systems, ensuring predictable interactions and data access.

Edge computing processes data closer to the source, reducing latency and bandwidth use compared to traditional cloud computing.

FairScale is a library for model parallelism and distributed training in deep learning.

Federated Distillation is a method for training AI models across decentralized data sources while preserving data privacy.

Gradient Compression reduces the size of gradient data during training to improve efficiency in distributed machine learning.

Horovod is an open-source framework for distributed deep learning training across multiple GPUs and machines.

Message Passing is a method for communication between processes in distributed systems or parallel computing.

A Multi-Agent System (MAS) is a system composed of multiple interacting agents that can solve problems collaboratively.

Network Synchronization ensures multiple systems or devices operate in unison, crucial for data integrity and performance.

Node Routing refers to the process of directing data packets through nodes in a network to reach their destination efficiently.

Online computation refers to processing data in real-time over the internet, enabling immediate results and interactions.

A Parameter Server is a distributed system for managing and sharing parameters in machine learning models.

Parameter Synchronization ensures consistency of model parameters across distributed systems in AI.

Ring AllReduce is a parallel computing technique used to efficiently aggregate data across distributed systems.

Routing-by-Agreement is a network routing protocol where nodes share and verify path information before data is sent.

A method enabling multiple parties to compute aggregated data without revealing individual contributions.