Albeit the popularity of federated learning (FL), recently emerging model-inversion and poisoning attacks arouse extensive concerns towards privacy or model integrity, which catalyzes the developments of secure federated learning (SFL) methods. Nonetheless, the collisions between its privacy and integrity, two equally crucial elements in collaborative learning scenarios, are relatively underexplored. Individuals' wish to “hide in the crowd” for privacy frequently clashes with aggregator' need to resist abnormal participants for integrity (i.e., the incompatibility between Byzantine robustness and differential privacy). The dilemma prompts researchers to reflect on how to build mutual confidence between individuals and aggregators. Against the backdrop, this paper proposes a multi-shuffler secure federated learning (MSFL) framework, based on which we further propound three modules (hierarchical shuffling mechanism, malice evaluation module, and composite defense strategy) to jointly guarantee strong privacy protection, efficient poisoning resistance, and agile adversary elimination. Extensive experiments on standard datasets exhibited the method's effectiveness in thwarting different FL poisoning attack paradigms with a minimal cost of privacy breaches.

Fairness-aware federated learning (FFL) plays a crucial role in mitigating bias against specific demographic groups (e.g., gender, race, and occupation) during collaborative training. Along with the ever-emerging new attack paradigms like gradient leakage and model poisoning, the reliability of FFL also obtains lots of research attention. Either UAV nodes or FFL aggregators could be untrusted adversaries. Although multiple security mechanisms involving encryption, obfuscation, Byzantine-robustness, and detection have been proposed, concrete to UAV networks, the majority of existing solutions are unfeasible due to high heterogeneity and limited resources among participants. Hence, in this article, we propose mutually reliable FFL (MR-FFL), a stratified community-based framework to facilitate privacy protection (FFL aggregator’s reliability) and poisoning elimination (client nodes’ reliability) jointly for FFL in heterogeneous UAV networks. We first divide UAV nodes into both peer communities (PCs) and colleague communities (CCs) according to cross-participant similarity and task-oriented fitness, respectively. Thus, the arbitrarily settled learning tasks following fair principles can be efficiently completed by fine-tuned CCs, even in the presence of a large degree of heterogeneity among PCs. Then, we integrate community-specific differential privacy into the MR-FFL process, to achieve privacy amplification as well as efficient and personal collaborative training at the same time. More importantly, we proposed a community-based credit evaluation to resist poisoning attacks in heterogeneous environments. The results on several standard data sets also highlight the performance of MR-Fed in terms of fairness, accuracy, and integrity jointly.

In virtue of the Internet of Things and Cobots, the Internet of Robotic Things (IoRT) significantly accelerates production efficiency and quality. As the scope and complexity of IoRT continue to expand, federated learning among massive robots is in urgent need. Nonetheless, this growing demand is accompanied by heightened threats to data privacy and model integrity. Besides, the heterogeneity among cross-silo robots compounds these challenges. In this paper, we discuss the key concerns of collaborative training in IoRT, and propose a shuffling-based moving target defense approach for federated learning in heterogeneous cross-silo IoRT environments (FedMTD). Based on the hierarchical training structure with node clustering, FedMTD bounds heterogeneity by domains, thereby minimizing the learning error and privacy loss. It also enhances resistance towards poisoning adversaries through decentralized credit evaluation. Finally, experimental results illustrate that FedMTD jointly demonstrates significant improvements in learning performance, privacy enhancement, and poisoning resistance.

HTTP live streaming delivers dynamically video content with varying bitrates to accommodate the dynamic real-time bandwidth fluctuations while considering diverse user preferences and device capabilities. Existing flow control solutions do not provide support for new features such as multi-source content transmission. In this paper, we propose a distributed multi-source rate control optimization algorithm (DMRCA) that maximizes the overall network bandwidth utility and improves viewer Quality of Experience (QoE). First, we model the rate control problem as a dual-optimized multi-source and multi-rate problem. Then, we decompose the problem into sub-problems of source rate selection and user rate adaptation and we prove that solving the original problem is equivalent to solving these two sub-problems. Furthermore, we propose DMRCA as a fully distributed algorithm to solve these sub-problems and derive an optimal solution and we discuss DMRCA’s complexity and convergence. Finally, through a series of simulation tests, we demonstrate the superiority of our proposed algorithm compared to alternative state-of-the-art solutions.