Quantized Federated Learning Model with Buffered Aggregation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Asynchronous Federated Learning with Buffered Aggregation (FedBuff) has high communication costs due to data heterogeneity and communication constraints in decentralized systems, particularly in sensor networks where nodes are communication-constrained and operate in a distributive manner.
Innovation Solution
The implementation of bidirectional quantized communication and buffered aggregation using a Quantized Federated Learning Model with Buffered Aggregation (QAFeL), which involves sampling client devices for local model updates, aggregating quantized model updates in a buffer, and performing global updates on a server model while reducing data transmission through quantization techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Federated Learning is implemented in decentralized systems, then data privacy and distributed learning capability are improved, but communication cost increases
Solution Approach 1:
The patent extracts only the essential model update information from client devices and transmits it to the server, rather than transmitting complete model parameters. This extraction of critical information reduces communication overhead while maintaining the federated learning privacy benefits.
Solution Approach 2:
The patent changes the parameter representation format by quantizing model updates to lower precision (e.g., 8-bit or 4-bit integers instead of 32-bit floats). This parameter transformation significantly reduces the communication bandwidth required while preserving sufficient model training accuracy.
2Loss of energy
If quantization is applied to model updates, then communication cost is reduced, but model update precision deteriorates
Solution Approach 1:
The patent applies preliminary quantization to model updates before transmission, and incorporates compensation mechanisms that anticipate and correct for quantization errors. This preliminary processing reduces communication costs while the compensation strategies mitigate precision loss.
Solution Approach 2:
The patent implements feedback mechanisms where the server receives quantized updates, aggregates them, and sends back correction signals or refined quantization parameters to clients. This feedback loop allows the system to adapt to quantization-induced precision errors and maintain model update accuracy despite reduced precision representation.
3Productivity
If buffered aggregation is used in asynchronous federated learning, then system scalability is improved, but communication delay increases
Solution Approach 1:
The patent implements a dynamic buffering mechanism where the buffer size and aggregation timing are adjusted based on system load, client availability, and communication conditions. This dynamic adaptation allows the system to scale efficiently while minimizing the impact of buffering delays on overall training performance.
Data Source
AI summary
Process and device configurations are provided for bidirectional quantized communication and buffered aggregation. In one embodiment, a method is provided for machine communication and to improve machine communication for machine learning models. Quantized communication by a server device can include sampling at least one client device to request a local model update using a hidden state, and receiving a quantized difference for a model of the at least one client device. The method can also include aggregating model updates in a buffer, performing a global update on a server model and updating the hidden state model using the server model. Quantized communication may be used for transmitting a hidden state update to the at least one client device. Processes and devices may also use Gossiped and quantized communication of hidden state model updates to at least one additional node to allow for Online Multi-Kernel Learning (OMKL).