  Maintaining the privacy of power system data is essential for protecting
sensitive information and ensuring the operation security of critical
infrastructure. Therefore, the adoption of centralized deep learning (DL)
transient stability assessment (TSA) frameworks can introduce risks to electric
utilities. This is because these frameworks make utility data susceptible to
cyber threats and communication issues when transmitting data to a central
server for training a single TSA model. Additionally, the centralized approach
demands significant computational resources, which may not always be readily
available. In light of these challenges, this paper introduces a federated
DL-based TSA framework designed to identify the operating states of the power
system. Instead of local utilities transmitting their data to a central server
for centralized model training, they independently train their own TSA models
using their respective datasets. Subsequently, the parameters of each local TSA
model are sent to a central server for model aggregation, and the resulting
model is shared back with the local clients. This approach not only preserves
the integrity of local utility data, making it resilient against cyber threats
but also reduces the computational demands for local TSA model training. The
proposed approach is tested on four local clients each having the IEEE 39-bus
test system.
