DIstributed Smart Communications with Verifiable EneRgy-optimal Yields
DISCOVERY is a coordinated research project conducted by teams from three different Spanish universities (UVigo, UPC, and UC3M) with complementary research skills. The project combines our knowledge in the areas of distributed learning, energy-efficiency, security, data privacy, communication protocols, and advanced services to face improvements in distributed communication protocols and systems with privacy and security protection mechanisms, jointly designed so that the digital carbon footprint (DCF) of the entire system can be characterized, measured, and optimized according to the users’ requirements.
The research goals in DISCOVERY will contribute to the realization of more efficient, secure, and sustainable distributed digital infrastructures currently used for data-driven smart services powered by statistical learning models.
Evaluation of the level of privacy in FL
The aim is to define privacy metrics in FL that establish how much information an active adversary can obtain a priori (before running a given number of interactions of the learning algorithm) and a posteriori (after running that number of interactions). Real-time privacy loss estimation methods will be developed.
Design of secure aggregation techniques in (D)FL to prevent attacks against the aggregator(s) or the learning network computation nodes
Homomorphic encryption, secure multiparty computation and differential privacy techniques will be applied. Much of these techniques provide post-quantum resistance in a natural way, since they are either based on hard problems over lattices, or provide theoretical security under certain assumptions of non-collusion.
Analysis of threat models in FL
The aim is to identify realistic threats in FL environments where active adversaries try to infer as much information as possible during the learning process.
Design of attacks with active adversaries in FL
We intend to design membership inference attacks (to know if a subject belongs to a training group) and property inference attacks (to validate if a record in the database satisfies a property).
Evaluation of the level of privacy in FL
The aim is to define privacy metrics in FL that establish how much information an active adversary can obtain a priori (before running a given number of interactions of the learning algorithm) and a posteriori (after running that number of interactions). Real-time privacy loss estimation methods will be developed.
Design of secure aggregation techniques in (D)FL to prevent attacks against the aggregator(s) or the learning network computation nodes
Homomorphic encryption, secure multiparty computation and differential privacy techniques will be applied. Much of these techniques provide post-quantum resistance in a natural way, since they are either based on hard problems over lattices, or provide theoretical security under certain assumptions of non-collusion.
Design of authentication and accountability algorithms in (D)FL
This involves integrating Blockchain, authentication and direct model sharing technologies into a decentralized learning architecture to ensure traceability of learning processes.
Improving the efficiency and robustness of (D)FL algorithms to deal with adverse situations such as failures, interruption or delay in communications, and statistical inhomogeneity in the data
Improving the efficiency and robustness of (D)FL algorithms to deal with adverse situations such as failures, interruption or delay in communications, and statistical inhomogeneity in the data. The aim is to optimize the cost of communication and computation while maintaining the models in an adequate degree of updating depending on their availability and the conditions of connection and delay in the network.
Create a toolbox to experiment countermeasures in the private, secure and robust operation of (D)FL systems
This software will be used to generate a prototype (use case) in the finance field to detect fraudulent transactions.
O1
Analysis of threat models in FL
The aim is to identify realistic threats in FL environments where active adversaries try to infer as much information as possible during the learning process.
O2
Design of attacks with active adversaries in FL
We intend to design membership inference attacks (to know if a subject belongs to a training group) and property inference attacks (to validate if a record in the database satisfies a property).
O3
Evaluation of the level of privacy in FL
The aim is to define privacy metrics in FL that establish how much information an active adversary can obtain a priori (before running a given number of interactions of the learning algorithm) and a posteriori (after running that number of interactions). Real-time privacy loss estimation methods will be developed.
O4
Design of secure aggregation techniques in (D)FL to prevent attacks against the aggregator(s) or the learning network computation nodes
Homomorphic encryption, secure multiparty computation and differential privacy techniques will be applied. Much of these techniques provide post-quantum resistance in a natural way, since they are either based on hard problems over lattices, or provide theoretical security under certain assumptions of non-collusion.
O5
Design of authentication and accountability algorithms in (D)FL
This involves integrating Blockchain, authentication and direct model sharing technologies into a decentralized learning architecture to ensure traceability of learning processes.
O6
Improving the efficiency and robustness of (D)FL algorithms to deal with adverse situations such as failures, interruption or delay in communications, and statistical inhomogeneity in the data
Improving the efficiency and robustness of (D)FL algorithms to deal with adverse situations such as failures, interruption or delay in communications, and statistical inhomogeneity in the data. The aim is to optimize the cost of communication and computation while maintaining the models in an adequate degree of updating depending on their availability and the conditions of connection and delay in the network.
O7
Create a toolbox to experiment countermeasures in the private, secure and robust operation of (D)FL systems
This software will be used to generate a prototype (use case) in the finance field to detect fraudulent transactions.
Partners
Rebeca P. Díaz Redondo
Principal Investigator
Fernando Pérez González
Principal Investigator
Manuel Fernández Veiga
Researcher
Ana Fernández Vilas
Researcher
Alberto Pedrouzo Ulloa
Researcher
Pedro Comesaña Alfaro
Researcher
David Vázquez Padín
Researcher
Results
Conference Papers
Future conference papers will be listed here.
Journal Papers
Future journal papers will be listed here.
News
Contact
Escola de enxeñería de telecomunicación
UVigo
- iclab@uvigo.gal
- +34 986813868
- Rúa Maxwell, s/n, 36310 Vigo