Communication-Efficient Multimodal Federated Learning: Joint Modality
  and Client Selection
Liangqi Yuan Dong-Jun Han Su Wang Devesh Upadhyay Christopher G. Brinton
Multimodal federated learning (FL) aims to enrich model training in FL
settings where clients are collecting measurements across multiple modalities.
However, key challenges to multimodal FL remain unaddressed, particularly in
heterogeneous network settings where: (i) the set of modalities collected by
each client will be diverse, and (ii) communication limitations prevent clients
from uploading all their locally trained modality models to the server. In this
paper, we propose multimodal Federated learning with joint Modality and Client
selection (mmFedMC), a new FL methodology that can tackle the above-mentioned
challenges in multimodal settings. The joint selection algorithm incorporates
two main components: (a) A modality selection methodology for each client,
which weighs (i) the impact of the modality, gauged by Shapley value analysis,
(ii) the modality model size as a gauge of communication overhead, against
(iii) the frequency of modality model updates, denoted recency, to enhance
generalizability. (b) A client selection strategy for the server based on the
local loss of modality model at each client. Experiments on five real-world
datasets demonstrate the ability of mmFedMC to achieve comparable accuracy to
several baselines while reducing the communication overhead by over 20x. A demo
video of our methodology is available at https://liangqiy.com/mmfedmc/.