A state-of-the-art survey on solving non-iid data in federated learning
Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can enable multiple clients to cooperatively train global models without …
researchers in that it can enable multiple clients to cooperatively train global models without …
[PDF][PDF] A review of speech-centric trustworthy machine learning: Privacy, safety, and fairness
Speech-centric machine learning systems have revolutionized a number of leading
industries ranging from transportation and healthcare to education and defense …
industries ranging from transportation and healthcare to education and defense …
Auditing privacy defenses in federated learning via generative gradient leakage
Federated Learning (FL) framework brings privacy benefits to distributed learning systems
by allowing multiple clients to participate in a learning task under the coordination of a …
by allowing multiple clients to participate in a learning task under the coordination of a …
Mime: Mimicking centralized stochastic algorithms in federated learning
Federated learning (FL) is a challenging setting for optimization due to the heterogeneity of
the data across different clients which gives rise to the client drift phenomenon. In fact …
the data across different clients which gives rise to the client drift phenomenon. In fact …
Breaking the centralized barrier for cross-device federated learning
Federated learning (FL) is a challenging setting for optimization due to the heterogeneity of
the data across different clients which gives rise to the client drift phenomenon. In fact …
the data across different clients which gives rise to the client drift phenomenon. In fact …
No fear of classifier biases: Neural collapse inspired federated learning with synthetic and fixed classifier
Data heterogeneity is an inherent challenge that hinders the performance of federated
learning (FL). Recent studies have identified the biased classifiers of local models as the key …
learning (FL). Recent studies have identified the biased classifiers of local models as the key …
On convergence of FedProx: Local dissimilarity invariant bounds, non-smoothness and beyond
The\FedProx~ algorithm is a simple yet powerful distributed proximal point optimization
method widely used for federated learning (FL) over heterogeneous data. Despite its …
method widely used for federated learning (FL) over heterogeneous data. Despite its …
A comprehensive empirical study of heterogeneity in federated learning
AM Abdelmoniem, CY Ho… - IEEE Internet of …, 2023 - ieeexplore.ieee.org
Federated learning (FL) is becoming a popular paradigm for collaborative learning over
distributed, private data sets owned by nontrusting entities. FL has seen successful …
distributed, private data sets owned by nontrusting entities. FL has seen successful …
Resource-adaptive federated learning with all-in-one neural composition
Abstract Conventional Federated Learning (FL) systems inherently assume a uniform
processing capacity among clients for deployed models. However, diverse client hardware …
processing capacity among clients for deployed models. However, diverse client hardware …
Training speech recognition models with federated learning: A quality/cost framework
We propose using federated learning, a decentralized on-device learning paradigm, to train
speech recognition models. By performing epochs of training on a per-user basis, federated …
speech recognition models. By performing epochs of training on a per-user basis, federated …