Poster Session Index

NAME POSTER TITLE SPOT
Hao WU Locally Differentially Private Frequency Estimation 1
Edwidge Cyffers Privacy Amplification by Decentralization  2
Fangshuo Liao On the Convergence of Shallow Neural Network Training with Randomly Masked Neurons 3
Yang Liu FEDGEMS: FEDERATED LEARNING OF LARGER SERVER MODELS VIA SELECTIVE KNOWLEDGE FUSION 4
Jianyu Wang Local Adaptivity in Federated Learning: Convergence and Consistency 5
Liam Collins Exploiting Shared Representations for Personalized Federated Learning 6
Aritra Mitra Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients 7
Cameron Wolfe GIST: Distributed Training for Large-Scale Graph Convolutional Networks 8
Edo Roth Private Distributed Analytics at Scale 9
Samuel Horvath Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout 10
Hanieh Hashemi Byzantine-Robust and Privacy-Preserving Framwork for FedML 11
Othmane Marfoq Federated Multi-Task Learning under a Mixture of Distributions 12
Mónica Ribero Federated Learning Under Time-Varying Communication Constraints and Intermittent Client Availability 13
Albert Cheu SHUFFLE PRIVATE VECTOR SUMS 14
Carolina Naim Private Multi-Group Aggregation 15
Adam Dziedzic Confidential and Private Collaborative Learning 16
Jonas Geiping Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models 17
Gavin Brown When Is Memorization of Irrelevant Training Data Required for High-Accuracy Learning? 18
Mohammad Taha Toghani Scalable Average Consensus with Compressed Communications 19
Saeedeh Parsaefard Robust Federated Learning by Mixture of Experts 20
Walid Saad Federated Learning and Wireless Networks: A Closer Union 21
Chen Dun ResIST: Layer-wise Decomposition of ResNets for Distributed Training 22
Mikko A. Heikkilä Tight accounting in the shuffle model of differential privacy 23
Ken Liu The Skellam Mechanism for Differentially Private Federated Learning 24
Yae Jee Cho Personalized Federated Learning (FL) for Heterogeneous Clients with Clustered Knowledge Transfer 25
Abhin Shah Optimal Compression of Locally Differentially Private Mechanism 26
Zheng Xu Practical and Private (Deep) Learning Without Sampling or Shuffling 27
Guari Joshi Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation 28
Peter Richtarik EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback 29
Jae Hun Ro FedJAX: Federated Learning Simulation with JAX 30
Ankit Singh Rawat FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients 31
Amir Houmansadr
Back to the Drawing Board: A Critical Evaluation of Poisining Attacks on Federated Learning 32
Giulia Fanti Reducing the Communication Cost of Federated Learning through Multistage Optimization 33
Borja De Balle Pigem Reconstructing Training Data with Informed Adversaries 34
Marco Canini RELAY: Resource-Efficient Federated Learning 35
Eugene Bagdasaryan Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation 36
Lun Wang FED-X2: Secure Federated Hypothesis Test 37