Federated Learning Node Selection via Reinforcement Learning
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Solution Overview
Problem
Existing federated machine learning techniques face inefficiencies and inaccuracies due to sub-optimal grouping of worker nodes based on dataset similarities, which can lead to exclusion of beneficial nodes and privacy violations, and require extensive pre-processing and manual work.
Innovation Solution
A method where a master node dynamically selects worker nodes for training using reinforcement learning to optimize the performance of a machine learning model based on previous training rounds, without sharing raw datasets, thus maintaining privacy and reducing manual effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If worker nodes are grouped into federations based on dataset similarities using techniques like cosine similarity or Euclidean distance, then the risk of parameter poisoning is reduced and training efficiency is improved, but the grouping may exclude beneficial worker nodes and violate privacy since it requires accessing local datasets
Solution Approach 1:
The system performs preliminary grouping of worker nodes into federations based on dataset characteristics before training begins. This pre-grouping prevents malicious nodes from being included in the training process from the start, thereby preventing parameter poisoning attacks before they can occur.
Solution Approach 2:
The system introduces an intermediary grouping mechanism that acts as a mediator between worker nodes and the training process. This intermediary layer evaluates worker node compatibility and creates federations without requiring direct access to local datasets, thus maintaining privacy while still enabling effective grouping.
2Productivity
If techniques for measuring dataset similarities are executed once before training begins, then the grouping is stable and simple to implement, but the grouping becomes sub-optimal when worker nodes join or leave the federation dynamically
Solution Approach 1:
The system implements dynamic federation grouping that adapts as worker nodes join or leave the system. The grouping is recalculated based on current federation performance and worker node characteristics, allowing the system to maintain optimal groupings even as the composition of worker nodes changes over time.
3Reliability
If worker nodes with diverse and balanced local datasets are grouped together, then the machine learning model benefits from varied data, but privacy is violated since it requires accessing and analyzing local datasets
Solution Approach 1:
The system introduces an intermediary mechanism that enables assessment of worker node dataset characteristics without direct access to the actual local datasets. This intermediary layer preserves privacy while still allowing the system to identify and group workers with diverse and balanced data distributions.
Data Source
AI summary
There is provided a method performed by a master node for managing training of a machine learning model. One or more worker nodes of a plurality of worker nodes are selected to train a machine learning model in a round of training. The one or more worker nodes are selected to optimize a performance of an updated machine learning model for a validation dataset after the round of training. The updated machine learning model has one or more parameters of the machine learning model trained by the one or more worker nodes in a previous round of training.