Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual
  Semantic Alignment-Based Contrastive Approach
Qingxiang Liu Sheng Sun Yuxuan Liang Jingjing Xue Min Liu
The existing federated learning (FL) methods for spatio-temporal forecasting
fail to capture the inherent spatio-temporal heterogeneity, which calls for
personalized FL (PFL) methods to model the spatio-temporally variant patterns.
While contrastive learning approach is promising in addressing spatio-temporal
heterogeneity, the existing methods are noneffective in determining negative
pairs and can hardly apply to PFL paradigm. To tackle this limitation, we
propose a novel PFL method, named Federated dUal sEmantic aLignment-based
contraStive learning (FUELS), which can adaptively align positive and negative
pairs based on semantic similarity, thereby injecting precise spatio-temporal
heterogeneity into the latent representation space by auxiliary contrastive
tasks. From temporal perspective, a hard negative filtering module is
introduced to dynamically align heterogeneous temporal representations for the
supplemented intra-client contrastive task. From spatial perspective, we design
lightweight-but-efficient prototypes as client-level semantic representations,
based on which the server evaluates spatial similarity and yields
client-customized global prototypes for the supplemented inter-client
contrastive task. Extensive experiments demonstrate that FUELS outperforms
state-of-the-art methods, with communication cost decreasing by around 94%.