Security and privacy taxonomy for attacks and defenses in Federated Learning (FL) - arxiv.org/pdf/2508.13730 Federated Learning (FL) [6] has emerged as a powerful paradigm enabling multiple clients (local nodes, parties, participants) to train ML models collaboratively without sharing raw data. While FL enhances data privacy, it also introduces unique security and privacy challenges that do not exist in traditional centralized learning settings, including vulnerabilities exacerbated by non-IID (non-Independent and Identically Distributed) data, where client datasets exhibit statistical heterogeneity in label, feature, or quantity distributions. Non-IID data amplifies security risks such as poisoning attacks, as adversaries can exploit skewed local updates to manipulate the global model, and privacy risks like membership inference, where attackers inferparticipation of specific data points by exploiting distributional disparities. Authors: Daniel M. Jimenez-Gutierrez, Yelizaveta Falkouskaya, José L. Hernández-Ramos, @arisana, @ichatzi, @avitaletti #FederatedLearning #PrivacyPreservingML #AISecurity #RobustAggregation #DifferentialPrivacy #SecureAggregation #ByzantineResilience #BackdoorAttacks #NonIID #AdversarialML #HomomorphicEncryption #SecureMPC #EdgeAI #DistributedAI #FLFrameworks

Wanna ensure an honest majority in the participants for #SecureAggregation and #DifferentialPrivacy in #FederatedLearning? Our #usesec24 work does it for you even when the server is malicious! Join us at Track 5 (Salon G) of @USENIXSecurity on Wed, Aug 14, at noon to learn more

As 1st Published on 14/12/23, here's the #Algorithm of the #BravoX Transform Formula. You can now #RUN the Neural Computer Program with your Federated Neural Training Data ( #FNTD ). TIPS: Apply the #SecureAggregation & #DifferentialPrivacy of the #LoBaFoDiSp Sequence.

#BravoX Chess Engine Presents #UDHG Ai-Sport (The Human Machine Chess Game) #ACSGPT #MachineLearning #DataScience #5FAGPT #DeepLearning #NeuralNetwork #FederatedLearning #NeuroGaming #AppliedChessScience #DifferentialPrivacy #SecureAggregation #LoBaFoDiSp #ArtificialIntelligence

The #SecureAggregation feature of the ACS-Web-X #FederatedPartition Program can be used to develop a #NeuralPasskey with 5FA; depending on the Neural Processor Speed of the ACSGPT AI Model (LAIM or GAIM), all the 5 #DifferentialPrivacy Sessions could run within 120 seconds.

Watch this explainer to learn about Owkin’s new research on SRATTA - a theoretical attack on #federatedlearning data protected by #secureaggregation - presented at @icmlconf 2023. To explore the full extent of SRATTA, read our latest blog at owkin.com/newsfeed/defending… #AI

Two days left of conference sessions at #ICML2023: Don’t miss the chance to talk to our Lead Research Scientist, @jeandut14000 about SRATTA – our latest research about the theoretical attack on data in #federatedlearning research that uses #secureaggregation. @icmlconf

Hear Owkin's Tanguy Marchand explain our latest paper on SRATTA - being presented at @icmlconf 2023. SRATTA describes a theoretical attack on #data protected by #secureaggregation in #federatedlearning. The attack can re-attribute data to its source, compromising #dataprivacy. By understanding SRATTA, the paper proposes clear counter-measures that users of federated learning can take to actively protect their data privacy during training. Read our blog to learn more at owkin.com/newsfeed/defending… Read the full paper at proceedings.mlr.press/v202/m…

Join Owkin in Hawaii this week at #ICML2023! At 5pm PDT tomorrow, catch Owkin's @jeandut14000 presenting a poster on our recent publication about #secureaggregation and the way Owkin has shown how to effectively mitigate potential attacks on #federatedlearning projects.

🤯 Lowkey Goated When #SecureAggregation Is The Vibe! 🔐 Check out this awesome paper by @xinchiqiu, @Renee42581826 et al. to learn more about efficient vertical federated learning! deepai.org/publication/effic…

Promising advancements in privacy-preserving machine learning - #FederatedLearning, #SecureAggregation, #DifferentialPrivacy, #HomomorphicEncryption. Scaling federated learning by Google bit.ly/2SwfX4i. Great overview by @jvmancuso @dropoutlabsai bit.ly/2MR2tea