  Learning an effective global model on private and decentralized datasets has
become an increasingly important challenge of machine learning when applied in
practice. Existing distributed learning paradigms, such as Federated Learning,
enable this via model aggregation which enforces a strong form of modeling
homogeneity and synchronicity across clients. This is however not suitable to
many practical scenarios. For example, in distributed sensing, heterogeneous
sensors reading data from different views of the same phenomenon would need to
use different models for different data modalities. Local learning therefore
happens in isolation but inference requires merging the local models to achieve
consensus. To enable consensus among local models, we propose a feature fusion
approach that extracts local representations from local models and incorporates
them into a global representation that improves the prediction performance.
Achieving this requires addressing two non-trivial problems. First, we need to
learn an alignment between similar feature components which are arbitrarily
arranged across clients to enable representation aggregation. Second, we need
to learn a consensus graph that captures the high-order interactions between
local feature spaces and how to combine them to achieve a better prediction.
This paper presents solutions to these problems and demonstrates them in
real-world applications on time series data such as power grids and traffic
networks.
