Byzantine machine learning: A primer
The problem of Byzantine resilience in distributed machine learning, aka Byzantine machine
learning, consists of designing distributed algorithms that can train an accurate model …
learning, consists of designing distributed algorithms that can train an accurate model …
On the privacy-robustness-utility trilemma in distributed learning
The ubiquity of distributed machine learning (ML) in sensitive public domain applications
calls for algorithms that protect data privacy, while being robust to faults and adversarial …
calls for algorithms that protect data privacy, while being robust to faults and adversarial …
Practical differentially private and byzantine-resilient federated learning
Privacy and Byzantine resilience are two indispensable requirements for a federated
learning (FL) system. Although there have been extensive studies on privacy and Byzantine …
learning (FL) system. Although there have been extensive studies on privacy and Byzantine …
Byzantine-robust federated learning with variance reduction and differential privacy
Federated learning (FL) is designed to preserve data privacy during model training, where
the data remains on the client side (ie, IoT devices), and only model updates of clients are …
the data remains on the client side (ie, IoT devices), and only model updates of clients are …
FedSIGN: A sign-based federated learning framework with privacy and robustness guarantees
Federated learning enables clients to train a global model jointly without sharing their
private local datasets. Despite its benefits, due to the untrustworthiness of clients and the …
private local datasets. Despite its benefits, due to the untrustworthiness of clients and the …
A multi-shuffler framework to establish mutual confidence for secure federated learning
Albeit the popularity of federated learning (FL), recently emerging model-inversion and
poisoning attacks arouse extensive concerns towards privacy or model integrity, which …
poisoning attacks arouse extensive concerns towards privacy or model integrity, which …
On the tradeoff between privacy preservation and Byzantine-robustness in decentralized learning
This paper jointly considers privacy preservation and Byzantine-robustness in decentralized
learning. In a decentralized network, honest-but-curious agents faithfully follow the …
learning. In a decentralized network, honest-but-curious agents faithfully follow the …
Accelerating wireless federated learning via nesterov's momentum and distributed principle component analysis
A wireless federated learning system is investigated by allowing a server and multiple
workers to exchange uncoded information via orthogonal wireless channels. Since the …
workers to exchange uncoded information via orthogonal wireless channels. Since the …
Practical homomorphic aggregation for byzantine ml
Due to the large-scale availability of data, machine learning (ML) algorithms are being
deployed in distributed topologies, where different nodes collaborate to train ML models …
deployed in distributed topologies, where different nodes collaborate to train ML models …
Lancelot: Towards efficient and privacy-preserving byzantine-robust federated learning within fully homomorphic encryption
In sectors such as finance and healthcare, where data governance is subject to rigorous
regulatory requirements, the exchange and utilization of data are particularly challenging …
regulatory requirements, the exchange and utilization of data are particularly challenging …