Private Agent Attribution Scoring for Efficient Federated Learning
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Solution Overview
Problem
In cross-silo federated learning deployments, selecting a subset of agents to contribute to a global model is challenging due to redundancy and inefficiency, with agents providing non-linear and inaccurate contributions, necessitating a method to determine agent data attribution and selection for efficient and accurate model training.
Innovation Solution
A computer-implemented method computes agent data attribution scores, selects a subset of agents based on these scores, and uses private sketches of data to exclude agents with low data diversity, employing synthetic data generation and simulation to improve model accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If all agents participate in cross-silo federated learning, then data diversity is maximized, but redundancy and inefficiency increase
Solution Approach 1:
The system performs preliminary actions by computing agent data attribution scores and identifying diverse agent subsets before the actual federated learning training begins. This pre-selection process determines which agents should participate to maximize data diversity while minimizing redundancy, avoiding unnecessary computational resources being wasted on agents that would contribute little to the global model.
Solution Approach 2:
The system applies local quality by evaluating and selecting agents based on their specific data characteristics and attribution scores. Each agent is assessed individually for their contribution to data diversity, and the selection process ensures that chosen agents have uniquely valuable data that complements other agents' data, rather than simply including all agents regardless of individual quality.
2Reliability
If all agents participate in cross-silo federated learning, then model accuracy may improve, but computational resources are wasted on redundant agents
Solution Approach 1:
The system performs preliminary computation of agent data attribution scores and identification of diverse agent subsets before training begins. This pre-determination of optimal agent selection allows the system to allocate computational resources efficiently during training, focusing only on agents whose data contributes meaningfully to model accuracy, thereby avoiding waste on redundant agents.
Solution Approach 2:
The system changes the parameter of agent selection from including all agents to selecting a optimized subset based on attribution scores and data diversity metrics. This parameter change enables the system to maintain model accuracy by focusing on high-value agents while significantly reducing computational resource consumption by excluding low-value redundant agents.
3Adaptability or versatility
If agents with low data diversity are included, then participation is maximized, but redundant processes increase
Solution Approach 1:
The system performs preliminary evaluation of agent data characteristics and computation of attribution scores before the federated learning process begins. This pre-screening identifies agents with low data diversity and excludes them from participation, preventing redundant processes from occurring in the first place rather than addressing them during training.
Solution Approach 2:
The system extracts and removes agents with low data diversity from the participant pool before training begins. By taking out these redundant agents in advance based on their attribution scores and data diversity metrics, the system simplifies the overall process by reducing the number of agents that need to be coordinated and processed during federated learning.
Data Source
AI summary
A computer-implemented method of determining an agent data attribution and selection to perform a collaborative data-related task includes computing an agent data attribution score for each agent of the plurality of agents associated with the collaborative data-related task. A subset of the plurality of agents that participate in the collaborative data-related task is selected based on the agent data attribution score. An instruction is transmitted to the selected subset of the plurality of agents for each agent to conduct a respective portion of the collaborative data-related task.