  Smart meter measurements, though critical for accurate demand forecasting,
face several drawbacks including consumers' privacy, data breach issues, to
name a few. Recent literature has explored Federated Learning (FL) as a
promising privacy-preserving machine learning alternative which enables
collaborative learning of a model without exposing private raw data for short
term load forecasting. Despite its virtue, standard FL is still vulnerable to
an intractable cyber threat known as Byzantine attack carried out by faulty
and/or malicious clients. Therefore, to improve the robustness of federated
short-term load forecasting against Byzantine threats, we develop a
state-of-the-art differentially private secured FL-based framework that ensures
the privacy of the individual smart meter's data while protect the security of
FL models and architecture. Our proposed framework leverages the idea of
gradient quantization through the Sign Stochastic Gradient Descent (SignSGD)
algorithm, where the clients only transmit the `sign' of the gradient to the
control centre after local model training. As we highlight through our
experiments involving benchmark neural networks with a set of Byzantine attack
models, our proposed approach mitigates such threats quite effectively and thus
outperforms conventional Fed-SGD models.
