Personalised Federated Learning On Heterogeneous Feature Spaces
This addresses a practical limitation in federated learning for real-world applications where clients use different data schemas, though it is an incremental advancement.
The paper tackles the problem of personalized federated learning when clients have heterogeneous feature spaces, proposing the FLIC framework to map data to a common space using local embeddings and federated Wasserstein barycenters, and shows performance improvements against benchmarks.
Most personalised federated learning (FL) approaches assume that raw data of all clients are defined in a common subspace i.e. all clients store their data according to the same schema. For real-world applications, this assumption is restrictive as clients, having their own systems to collect and then store data, may use heterogeneous data representations. We aim at filling this gap. To this end, we propose a general framework coined FLIC that maps client's data onto a common feature space via local embedding functions. The common feature space is learnt in a federated manner using Wasserstein barycenters while the local embedding functions are trained on each client via distribution alignment. We integrate this distribution alignement mechanism into a federated learning approach and provide the algorithmics of FLIC. We compare its performances against FL benchmarks involving heterogeneous input features spaces. In addition, we provide theoretical insights supporting the relevance of our methodology.