Meta-Learning Layer
The meta-learning layer implements cross-domain adaptation. It effectively allows the system to: - Learn a pattern in Domain A. - Apply that knowledge to accelerate learning in Domain B.
MetaLearningAgent
The high-level orchestrator. It manages the lifecycle of knowledge transfer between the local domain and the global federation.
Processing Flow
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Domain Updates Collect performance metrics (
ΔL) from individual domains. -
Cross-Domain Aggregation Combine updates using weighted averaging based on domain similarity.
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Online Optimization Apply Reptile-style meta-gradient updates to the global prior.
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Rollback Mechanism If a meta-update degrades performance, the system automatically reverts.
Online Reptile Optimizer
We implement an Online version of the Reptile algorithm for fast adaptation.
The Concept
Instead of optimizing for a specific task, we optimize for Initial Parameters that are easy to fine-tune.
The update rule focuses on moving the initialization point (θ) towards the parameters (φ) that worked well for specific tasks.
Update Rule:
θ_t+1 = θ_t + α · (φ_i - θ_t)
Variables: * θ_t: Current global meta-parameters. * φ_i: Task-specific parameters after local Training. * α: Meta-learning rate (Adaptive).