Advances and open problems in federated learning
Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …
devices or whole organizations) collaboratively train a model under the orchestration of a …
Edge artificial intelligence for 6G: Vision, enabling technologies, and applications
The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …
wireless networks. It has been envisioned that 6G will be transformative and will …
Shuffled model of differential privacy in federated learning
We consider a distributed empirical risk minimization (ERM) optimization problem with
communication efficiency and privacy requirements, motivated by the federated learning …
communication efficiency and privacy requirements, motivated by the federated learning …
Hiding among the clones: A simple and nearly optimal analysis of privacy amplification by shuffling
Recent work of Erlingsson, Feldman, Mironov, Raghunathan, Talwar, and Thakurta 1
demonstrates that random shuffling amplifies differential privacy guarantees of locally …
demonstrates that random shuffling amplifies differential privacy guarantees of locally …
The fundamental price of secure aggregation in differentially private federated learning
We consider the problem of training a $ d $ dimensional model with distributed differential
privacy (DP) where secure aggregation (SecAgg) is used to ensure that the server only sees …
privacy (DP) where secure aggregation (SecAgg) is used to ensure that the server only sees …
The poisson binomial mechanism for unbiased federated learning with secure aggregation
Abstract We introduce the Poisson Binomial mechanism (PBM), a discrete differential
privacy mechanism for distributed mean estimation (DME) with applications to federated …
privacy mechanism for distributed mean estimation (DME) with applications to federated …
Privacy amplification via compression: Achieving the optimal privacy-accuracy-communication trade-off in distributed mean estimation
Privacy and communication constraints are two major bottlenecks in federated learning (FL)
and analytics (FA). We study the optimal accuracy of mean and frequency estimation …
and analytics (FA). We study the optimal accuracy of mean and frequency estimation …
Optimal algorithms for mean estimation under local differential privacy
We study the problem of mean estimation of $\ell_2 $-bounded vectors under the constraint
of local differential privacy. While the literature has a variety of algorithms that achieve the …
of local differential privacy. While the literature has a variety of algorithms that achieve the …
SoteriaFL: A unified framework for private federated learning with communication compression
To enable large-scale machine learning in bandwidth-hungry environments such as
wireless networks, significant progress has been made recently in designing communication …
wireless networks, significant progress has been made recently in designing communication …
Trading off privacy, utility, and efficiency in federated learning
Federated learning (FL) enables participating parties to collaboratively build a global model
with boosted utility without disclosing private data information. Appropriate protection …
with boosted utility without disclosing private data information. Appropriate protection …