-
Enhancing Privacy via Hierarchical Federated Learning
Federated learning suffers from several privacy-related issues that expo...
read it
-
Fed+: A Family of Fusion Algorithms for Federated Learning
We present a class of methods for federated learning, which we call Fed+...
read it
-
Scalable Federated Learning over Passive Optical Networks
Two-step aggregation is introduced to facilitate scalable federated lear...
read it
-
FedMD: Heterogenous Federated Learning via Model Distillation
Federated learning enables the creation of a powerful centralized model ...
read it
-
Overcoming Forgetting in Federated Learning on Non-IID Data
We tackle the problem of Federated Learning in the non i.i.d. case, in w...
read it
-
Hierarchically Fair Federated Learning
Federated learning facilitates collaboration among self-interested agent...
read it
-
Beyond privacy regulations: an ethical approach to data usage in transportation
With the exponential advancement of business technology in recent years,...
read it
A Real-time Contribution Measurement Method for Participants in Federated Learning
In recent years, individuals, business organizations or the country have paid more and more attention to their data privacy. At the same time, with the rise of federated learning, federated learning is involved in more and more fields. However, there is no good evaluation standard for each agent participating in federated learning. This paper proposes an online evaluation method for federated learning and compares it with the results obtained by Shapley Value in game theory. The method proposed in this paper is more sensitive to data quality and quantity.
READ FULL TEXT
Comments
There are no comments yet.