  Federated learning alleviates the privacy risk in distributed learning by
transmitting only the local model updates to the central server. However, it
faces challenges including statistical heterogeneity of clients' datasets and
resource constraints of client devices, which severely impact the training
performance and user experience. Prior works have tackled these challenges by
combining personalization with model compression schemes including quantization
and pruning. However, the pruning is data-dependent and thus must be done on
the client side which requires considerable computation cost. Moreover, the
pruning normally trains a binary supermask $\in \{0, 1\}$ which significantly
limits the model capacity yet with no computation benefit. Consequently, the
training requires high computation cost and a long time to converge while the
model performance does not pay off. In this work, we propose HideNseek which
employs one-shot data-agnostic pruning at initialization to get a subnetwork
based on weights' synaptic saliency. Each client then optimizes a sign
supermask $\in \{-1, +1\}$ multiplied by the unpruned weights to allow faster
convergence with the same compression rates as state-of-the-art. Empirical
results from three datasets demonstrate that compared to state-of-the-art,
HideNseek improves inferences accuracies by up to 40.6\% while reducing the
communication cost and training time by up to 39.7\% and 46.8\% respectively.
