Recent achievements of linked data implementations and increased number of datasets available on the web as linked data, has given rise to the need and tendency toward processing federated queries over these datasets. Due to distribution of linked data across the web, the methods that process federated queries through a distributed approach are more attractive to users and have gained more prosperity. In distributed processing of federated queries, we need methods and procedures to execute the query in an optimal manner. Most existing methods perform the optimization task based on statistical information, whereas the query processor does not have precise statistical information about their properties, since the data sources are autonomous. When precise statistics are not available, the possibility of wrong estimation highly increases and may lead to inefficient execution of query at runtime. Another problem of existing methods is that in optimization phase, they assume that runtime conditions of query execution are stable, while the environment in which federated queries are executed over linked data is dynamic and non-predictable. By considering these two problems, there is a great potential for exploiting query processing techniques in an adaptive manner. In this paper, an adaptive method is proposed for processing federated queries over linked data which is based on the concept of routing the tuples. The proposed method is able to execute the query effectively without any need to prior statistical information. This method can change the query execution plan at runtime so that less intermediate results are produced, and it can also adapt the execution plan to new situation if unpredicted network latencies arise. Evaluation of our method by running real queries over well-known linked datasets shows very good results especially for complex queries. Keywords: 1- Processing Federated Queries over Linked Data 2- Linked Data 3- Adaptive Query Processing 4- Federation of SPARQL Endpoints