Context-Aware Inference via Performance Forecasting in Decentralized Learning Networks
This work addresses the need for faster adaptation to changing conditions in decentralized learning, offering a predictive approach that could benefit various applications requiring dynamic model weighting.
The paper tackles the problem of slow adaptation in decentralized learning networks by developing a performance forecasting model that predicts future model accuracy to assign weights proactively, showing that forecasting regret or regret z-score improves inference accuracy compared to loss-based forecasting or naive averaging.
In decentralized learning networks, predictions from many participants are combined to generate a network inference. While many studies have demonstrated performance benefits of combining multiple model predictions, existing strategies using linear pooling methods (ranging from simple averaging to dynamic weight updates) face a key limitation. Dynamic prediction combinations that rely on historical performance to update weights are necessarily reactive. Due to the need to average over a reasonable number of epochs (with moving averages or exponential weighting), they tend to be slow to adjust to changing circumstances (phase or regime changes). In this work, we develop a model that uses machine learning to forecast the performance of predictions by models at each epoch in a time series. This enables `context-awareness' by assigning higher weight to models that are likely to be more accurate at a given time. We show that adding a performance forecasting worker in a decentralized learning network, following a design similar to the Allora network, can improve the accuracy of network inferences. Specifically, we find forecasting models that predict regret (performance relative to the network inference) or regret z-score (performance relative to other workers) show greater improvement than models predicting losses, which often do not outperform the naive network inference (historically weighted average of all inferences). Through a series of optimization tests, we show that the performance of the forecasting model can be sensitive to choices in the feature set and number of training epochs. These properties may depend on the exact problem and should be tailored to each domain. Although initially designed for a decentralized learning network, using performance forecasting for prediction combination may be useful in any situation where predictive rather than reactive model weighting is needed.