Year | Title | Conference / Journal |
---|---|---|
2025 | FedDiAL: Adaptive Federated Learning with Hierarchical Discriminative Network for Large Pre-trained Models | ACM KDD |
2024 | Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updates | MobiHoc |
2024 | Enhancing Model Poisoning Attacks to Byzantine-Robust Federated Learning via Critical Learning Periods | RAID |
2024 | FedRoLA: Robust Federated Learning Against Model Poisoning via Layer-based Aggregation | ACM KDD |
2024 | DePRL: Achieving Linear Convergence Speedup in Personalized Decentralized Learning with Shared Representations | AAAI |
2023 | CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning | ACM KDD |
2023 | DeFL: Defending Against Model Poisoning Attacks in Federated Learning via Critical Learning Periods Awareness | AAAI |
2022 | Towards Latency Awareness for Content Delivery Network Caching | USENIX ATC |
2022 | Reinforcement Learning for Dynamic Dimensioning of Cloud Caches: A Restless Bandit Approach | IEEE/ACM TON |
2022 | Seizing Critical Learning Periods in Federated Learning | AAAI |
2021 | Learning from Optimal Caching for Content Delivery | ACM CoNEXT |
2020 | RL-Bélády: A Unified Learning Framework for Content Caching | ACM MM |
A prototype recommendation system based on federated learning.
A secure aggregation service with differential privacy support.
High-performance network optimization simulation and visualization tools.