… a robust design for federated learning to decline the effect of noise. Considering the … In
this section, we consider the robust design in federated learning using the expectation-based …

… Since the label noise comes from the feedback by other … contribution of noisy clients in
federated communication. CCR … and important robust federated learning problem with noisy and …

… federated learning scenario, where local data have noisy labels. We present a new federated
learning … the effect of noisy data. Our approach maintains the high performance on various …

… the communication. Next, we consider the communication constraints of federated learning
in … More specifically, we propose two communication-efficient hypergradient estimators, ie the …

… SYSTEM MODEL In this section, we recall the standard federated learning model while also
… Federated Learning Model We can formally describe the training phase in FL as minimizing …

… the robust federated learning algorithm for the stochastic learning of additive models with
least squares. We also offer two variants of RFA: a faster one with one-step robust aggregation, …

We study federated learning (FL), where power-limited wireless devices utilize their local
datasets to collaboratively train a global model with the help of a remote parameter server (PS). …

… The federated learning problem setting studied in this paper mostly follows the standard
model in the original paper [2]. In particular, we consider a FL system with one central parameter …

… for federated learning. In this work we propose FHDnn, a synergetic federated learning …
FHDnn performs hyperdimensional learning on features extracted from a self-supervised …

… more robust to this type of data do not compress the downstream (see Section V). We will
then proceed to construct a new efficient communication protocol for federated learning that …