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Found 8 papers, 6 papers with code
Scalable Data Point Valuation in Decentralized Learning
The valuation of data points through DDVal allows to also draw hierarchical conclusions on the contribution of institutions, and we empirically show that the accuracy of DDVal in estimating institutional contributions is higher than existing Shapley value approximation methods for federated learning.
GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning
Federated learning (FL) is an effective technique to directly involve edge devices in machine learning training while preserving client privacy.
Generalizing Neural Networks by Reflecting Deviating Data in Production
However, inputs may deviate from the training dataset distribution in real deployments.
Fairness-guided SMT-based Rectification of Decision Trees and Random Forests
Data-driven decision making is gaining prominence with the popularity of various machine learning models.
Iroko: A Framework to Prototype Reinforcement Learning for Data Center Traffic Control
We present a new emulator, Iroko, which we developed to support different network topologies, congestion control algorithms, and deployment scenarios.
Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning
Federated Learning is the current state of the art in supporting secure multi-party machine learning (ML): data is maintained on the owner's device and the updates to the model are aggregated through a secure protocol.
Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting
Distributed machine learning (ML) systems today use an unsophisticated threat model: data sources must trust a central ML process.
Mitigating Sybils in Federated Learning Poisoning
Unfortunately, such approaches are susceptible to a variety of attacks, including model poisoning, which is made substantially worse in the presence of sybils.
