<p>How is the test accuracy calculated? How many local epochs are the clients training?</p>
<p>If the global model is tested on a held out set of examples, it is possible that clients are detrimentally overfitting during local training. As the global model approaches convergence, each client ends up training a model that works well for them individually, but may be diverging from the optimal global model (sometimes called <em>client drift</em> <a href="https://arxiv.org/abs/1910.06378" rel="nofollow noreferrer">https://arxiv.org/abs/1910.06378</a>). This is may occur when the client's local dataset has a distribution very different from the global distribution and more likely when the client learning rates are high (<a href="https://arxiv.org/abs/2007.00878" rel="nofollow noreferrer">https://arxiv.org/abs/2007.00878</a>).</p>
<p>Decreasing the client learning rate, reducing the number of steps/batches, and other methods that cause the clients to do less &quot;work&quot; per communication round may reduce the fluctuation.</p>
TFF : test accuracy fluctuate
<p>I train a ResNet50 model with TFF, I use test accuracy on test data for evaluation, but I find many fluctuations as shown in the figure below, So please how can I avoid this fluctuation ?</p>
<p><a href="https://i.stack.imgur.com/JDtmg.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/JDtmg.png" alt="enter image description here" /></a></p>