  Federated edge learning (FEEL) is a promising distributed learning technique
for next-generation wireless networks. FEEL preserves the user's privacy,
reduces the communication costs, and exploits the unprecedented capabilities of
edge devices to train a shared global model by leveraging a massive amount of
data generated at the network edge. However, FEEL might significantly shorten
energy-constrained participating devices' lifetime due to the power consumed
during the model training round. This paper proposes a novel approach that
endeavors to minimize computation and communication energy consumption during
FEEL rounds to address this issue. First, we introduce a modified local
training algorithm that intelligently selects only the samples that enhance the
model's quality based on a predetermined threshold probability. Then, the
problem is formulated as joint energy minimization and resource allocation
optimization problem to obtain the optimal local computation time and the
optimal transmission time that minimize the total energy consumption
considering the worker's energy budget, available bandwidth, channel states,
beamforming, and local CPU speed. After that, we introduce a tractable solution
to the formulated problem that ensures the robustness of FEEL. Our simulation
results show that our solution substantially outperforms the baseline FEEL
algorithm as it reduces the local consumed energy by up to 79%.
